Publication statistics

Pub. period:1998-2011
Pub. count:95
Number of co-authors:155



Co-authors

Number of publications with 3 favourite co-authors:

Xing Xie:22
Lei Zhang:17
Zheng Chen:17

 

 

Productive colleagues

Wei-Ying Ma's 3 most productive colleagues in number of publications:

Hsinchun Chen:145
Edward A. Fox:109
Zheng Chen:62
 
 
 
Jul 30

It's all about one thing: creative problem-solving to get the story out.

-- Robert Greenberg, R/GA, 2006

 
 

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Wei-Ying Ma

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2011
 
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Zheng, Yu, Zhang, Lizhu, Ma, Zhengxin, Xie, Xing and Ma, Wei-Ying (2011): Recommending friends and locations based on individual location history. In ACM Transactions on the Web, 5 (1) p. 5.

The increasing availability of location-acquisition technologies (GPS, GSM networks, etc.) enables people to log the location histories with spatio-temporal data. Such real-world location histories imply, to some extent, users' interests in places, and bring us opportunities to understand the correlation between users and locations. In this article, we move towards this direction and report on a personalized friend and location recommender for the geographical information systems (GIS) on the Web. First, in this recommender system, a particular individual's visits to a geospatial region in the real world are used as their implicit ratings on that region. Second, we measure the similarity between users in terms of their location histories and recommend to each user a group of potential friends in a GIS community. Third, we estimate an individual's interests in a set of unvisited regions by involving his/her location history and those of other users. Some unvisited locations that might match their tastes can be recommended to the individual. A framework, referred to as a hierarchical-graph-based similarity measurement (HGSM), is proposed to uniformly model each individual's location history, and effectively measure the similarity among users. In this framework, we take into account three factors: 1) the sequence property of people's outdoor movements, 2) the visited popularity of a geospatial region, and 3) the hierarchical property of geographic spaces. Further, we incorporated a content-based method into a user-based collaborative filtering algorithm, which uses HGSM as the user similarity measure, to estimate the rating of a user on an item. We evaluated this recommender system based on the GPS data collected by 75 subjects over a period of 1 year in the real world. As a result, HGSM outperforms related similarity measures, namely similarity-by-count, cosine similarity, and Pearson similarity measures. Moreover, beyond the item-based CF method and random recommendations, our system provides users with more attractive locations and better user experiences of recommendation.

© All rights reserved Zheng et al. and/or ACM

2010
 
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Feng, Guwen, Wang, Xin-Jing, Zhang, Lei and Ma, Wei-Ying (2010): Mining adjacent markets from a large-scale ads video collection for image advertising. In: Proceedings of the 33rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2010. pp. 893-894.

The research on image advertising is still in its infancy. Most previous approaches suggest ads by directly matching an ad to a query image, which lacks the power to identify ads from adjacent market. In this paper, we tackle the problem by mining knowledge on adjacent markets from ads videos with a novel Multi-Modal Dirichlet Process Mixture Sets model, which is a unified model of (video frames) clustering and (ads) ranking. Our approach is not only capable of discovering relevant ads (e.g. car ads for a query car image), but also suggesting ads from adjacent markets (e.g. tyre ads). Experimental results show that our proposed approach is fairly effective.

© All rights reserved Feng et al. and/or their publisher

 
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Ren, Yuheng, Yu, Mo, Wang, Xin-Jing, Zhang, Lei and Ma, Wei-Ying (2010): Diversifying landmark image search results by learning interested views from community photos. In: Proceedings of the 2010 International Conference on the World Wide Web 2010. pp. 1289-1292.

In this paper, we demonstrate a novel landmark photo search and browsing system: Agate, which ranks landmark image search results considering their relevance, diversity and quality. Agate learns from community photos the most interested aspects and related activities of a landmark, and generates adaptively a Table of Content (TOC) as a summary of the attractions to facilitate the user browsing. Image search results are thus re-ranked with the TOC so as to ensure a quick overview of the attractions of the landmarks. A novel non-parametric TOC generation and set-based ranking algorithm, MoM-DPM Sets, is proposed as the key technology of Agate. Experimental results based on human evaluation show the effectiveness of our model and users' preference for Agate.

© All rights reserved Ren et al. and/or their publisher

 
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Zheng, Yu, Chen, Yukun, Li, Quannan, Xie, Xing and Ma, Wei-Ying (2010): Understanding transportation modes based on GPS data for web applications. In ACM Transactions on the Web, 4 (1) p. 1.

User mobility has given rise to a variety of Web applications, in which the global positioning system (GPS) plays many important roles in bridging between these applications and end users. As a kind of human behavior, transportation modes, such as walking and driving, can provide pervasive computing systems with more contextual information and enrich a user's mobility with informative knowledge. In this article, we report on an approach based on supervised learning to automatically infer users' transportation modes, including driving, walking, taking a bus and riding a bike, from raw GPS logs. Our approach consists of three parts: a change point-based segmentation method, an inference model and a graph-based post-processing algorithm. First, we propose a change point-based segmentation method to partition each GPS trajectory into separate segments of different transportation modes. Second, from each segment, we identify a set of sophisticated features, which are not affected by differing traffic conditions (e.g., a person's direction when in a car is constrained more by the road than any change in traffic conditions). Later, these features are fed to a generative inference model to classify the segments of different modes. Third, we conduct graph-based postprocessing to further improve the inference performance. This postprocessing algorithm considers both the commonsense constraints of the real world and typical user behaviors based on locations in a probabilistic manner. The advantages of our method over the related works include three aspects. (1) Our approach can effectively segment trajectories containing multiple transportation modes. (2) Our work mined the location constraints from user-generated GPS logs, while being independent of additional sensor data and map information like road networks and bus stops. (3) The model learned from the dataset of some users can be applied to infer GPS data from others. Using the GPS logs collected by 65 people over a period of 10 months, we evaluated our approach via a set of experiments. As a result, based on the change-point-based segmentation method and Decision Tree-based inference model, we achieved prediction accuracy greater than 71 percent. Further, using the graph-based post-processing algorithm, the performance attained a 4-percent enhancement.

© All rights reserved Zheng et al. and/or ACM

 
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Xiao, Xiangye, Luo, Qiong, Li, Zhisheng, Xie, Xing and Ma, Wei-Ying (2010): A large-scale study on map search logs. In ACM Transactions on the Web, 4 (3) p. 8.

Map search engines, such as Google Maps, Yahoo! Maps, and Microsoft Live Maps, allow users to explicitly specify a target geographic location, either in keywords or on the map, and to search businesses, people, and other information of that location. In this article, we report a first study on a million-entry map search log. We identify three key attributes of a map search record -- the keyword query, the target location and the user location, and examine the characteristics of these three dimensions separately as well as the associations between them. Comparing our results with those previously reported on logs of general search engines and mobile search engines, including those for geographic queries, we discover the following unique features of map search: (1) People use longer queries and modify queries more frequently in a session than in general search and mobile search; People view fewer result pages per query than in general search; (2) The popular query topics in map search are different from those in general search and mobile search; (3) The target locations in a session change within 50 kilometers for almost 80% of the sessions; (4) Queries, search target locations and user locations (both at the city level) all follow the power law distribution; (5) One third of queries are issued for target locations within 50 kilometers from the user locations; (6) The distribution of a query over target locations appears to follow the geographic location of the queried entity.

© All rights reserved Xiao et al. and/or ACM

2009
 
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Yang, Jiang-Ming, Cai, Rui, Wang, Yida, Zhu, Jun, Zhang, Lei and Ma, Wei-Ying (2009): Incorporating site-level knowledge to extract structured data from web forums. In: Proceedings of the 2009 International Conference on the World Wide Web 2009. pp. 181-190.

Web forums have become an important data resource for many web applications, but extracting structured data from unstructured web forum pages is still a challenging task due to both complex page layout designs and unrestricted user created posts. In this paper, we study the problem of structured data extraction from various web forum sites. Our target is to find a solution as general as possible to extract structured data, such as post title, post author, post time, and post content from any forum site. In contrast to most existing information extraction methods, which only leverage the knowledge inside an individual page, we incorporate both page-level and site-level knowledge and employ Markov logic networks (MLNs) to effectively integrate all useful evidence by learning their importance automatically. Site-level knowledge includes (1) the linkages among different object pages, such as list pages and post pages, and (2) the interrelationships of pages belonging to the same object. The experimental results on 20 forums show a very encouraging information extraction performance, and demonstrate the ability of the proposed approach on various forums. We also show that the performance is limited if only page-level knowledge is used, while when incorporating the site-level knowledge both precision and recall can be significantly improved.

© All rights reserved Yang et al. and/or ACM Press

 
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Zheng, Yu, Zhang, Lizhu, Xie, Xing and Ma, Wei-Ying (2009): Mining interesting locations and travel sequences from GPS trajectories. In: Proceedings of the 2009 International Conference on the World Wide Web 2009. pp. 791-800.

The increasing availability of GPS-enabled devices is changing the way people interact with the Web, and brings us a large amount of GPS trajectories representing people's location histories. In this paper, based on multiple users' GPS trajectories, we aim to mine interesting locations and classical travel sequences in a given geospatial region. Here, interesting locations mean the culturally important places, such as Tiananmen Square in Beijing, and frequented public areas, like shopping malls and restaurants, etc. Such information can help users understand surrounding locations, and would enable travel recommendation. In this work, we first model multiple individuals' location histories with a tree-based hierarchical graph (TBHG). Second, based on the TBHG, we propose a HITS (Hypertext Induced Topic Search)-based inference model, which regards an individual's access on a location as a directed link from the user to that location. This model infers the interest of a location by taking into account the following three factors. 1) The interest of a location depends on not only the number of users visiting this location but also these users' travel experiences. 2) Users' travel experiences and location interests have a mutual reinforcement relationship. 3) The interest of a location and the travel experience of a user are relative values and are region-related. Third, we mine the classical travel sequences among locations considering the interests of these locations and users' travel experiences. We evaluated our system using a large GPS dataset collected by 107 users over a period of one year in the real world. As a result, our HITS-based inference model outperformed baseline approaches like rank-by-count and rank-by-frequency. Meanwhile, when considering the users' travel experiences and location interests, we achieved a better performance beyond baselines, such as rank-by-count and rank-by-interest, etc.

© All rights reserved Zheng et al. and/or ACM Press

 
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Xiao, Xiangye, Luo, Qiong, Hong, Dan, Fu, Hongbo, Xie, Xing and Ma, Wei-Ying (2009): Browsing on small displays by transforming Web pages into hierarchically structured subpages. In ACM Transactions on the Web, 3 (1) p. 4.

We propose a new Web page transformation method to facilitate Web browsing on handheld devices such as Personal Digital Assistants (PDAs). In our approach, an original Web page that does not fit on the screen is transformed into a set of subpages, each of which fits on the screen. This transformation is done through slicing the original page into page blocks iteratively, with several factors considered. These factors include the size of the screen, the size of each page block, the number of blocks in each transformed page, the depth of the tree hierarchy that the transformed pages form, as well as the semantic coherence between blocks. We call the tree hierarchy of the transformed pages an SP-tree. In an SP-tree, an internal node consists of a textually enhanced thumbnail image with hyperlinks, and a leaf node is a block extracted from a subpage of the original Web page. We adaptively adjust the fanout and the height of the SP-tree so that each thumbnail image is clear enough for users to read, while at the same time, the number of clicks needed to reach a leaf page is few. Through this transformation algorithm, we preserve the contextual information in the original Web page and reduce scrolling. We have implemented this transformation module on a proxy server and have conducted usability studies on its performance. Our system achieved a shorter task completion time compared with that of transformations from the Opera browser in nine of ten tasks. The average improvement on familiar pages was

© All rights reserved Xiao et al. and/or ACM

2008
 
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Xu, Jun, Liu, Tie-Yan, Lu, Min, Li, Hang and Ma, Wei-Ying (2008): Directly optimizing evaluation measures in learning to rank. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2008. pp. 107-114.

One of the central issues in learning to rank for information retrieval is to develop algorithms that construct ranking models by directly optimizing evaluation measures used in information retrieval such as Mean Average Precision (MAP) and Normalized Discounted Cumulative Gain (NDCG). Several such algorithms including SVMmap and AdaRank have been proposed and their effectiveness has been verified. However, the relationships between the algorithms are not clear, and furthermore no comparisons have been conducted between them. In this paper, we conduct a study on the approach of directly optimizing evaluation measures in learning to rank for Information Retrieval (IR). We focus on the methods that minimize loss functions upper bounding the basic loss function defined on the IR measures. We first provide a general framework for the study and analyze the existing algorithms of SVMmap and AdaRank within the framework. The framework is based on upper bound analysis and two types of upper bounds are discussed. Moreover, we show that we can derive new algorithms on the basis of this analysis and create one example algorithm called PermuRank. We have also conducted comparisons between SVMmap, AdaRank, PermuRank, and conventional methods of Ranking SVM and RankBoost, using benchmark datasets. Experimental results show that the methods based on direct optimization of evaluation measures can always outperform conventional methods of Ranking SVM and RankBoost. However, no significant difference exists among the performances of the direct optimization methods themselves.

© All rights reserved Xu et al. and/or ACM Press

 
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Wang, Yida, Yang, Jiang-Ming, Lai, Wei, Cai, Rui, Zhang, Lei and Ma, Wei-Ying (2008): Exploring traversal strategy for web forum crawling. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2008. pp. 459-466.

In this paper, we study the problem of Web forum crawling. Web forum has now become an important data source of many Web applications; while forum crawling is still a challenging task due to complex in-site link structures and login controls of most forum sites. Without carefully selecting the traversal path, a generic crawler usually downloads many duplicate and invalid pages from forums, and thus wastes both the precious bandwidth and the limited storage space. To crawl forum data more effectively and efficiently, in this paper, we propose an automatic approach to exploring an appropriate traversal strategy to direct the crawling of a given target forum. In detail, the traversal strategy consists of the identification of the skeleton links and the detection of the page-flipping links. The skeleton links instruct the crawler to only crawl valuable pages and meanwhile avoid duplicate and uninformative ones; and the page-flipping links tell the crawler how to completely download a long discussion thread which is usually shown in multiple pages in Web forums. The extensive experimental results on several forums show encouraging performance of our approach. Following the discovered traversal strategy, our forum crawler can archive more informative pages in comparison with previous related work and a commercial generic crawler.

© All rights reserved Wang et al. and/or ACM Press

 
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Yang, Jiang-Ming, Cai, Rui, Jing, Feng, Wang, Shuo, Zhang, Lei and Ma, Wei-Ying (2008): Search-based query suggestion. In: Shanahan, James G., Amer-Yahia, Sihem, Manolescu, Ioana, Zhang, Yi, Evans, David A., Kolcz, Aleksander, Choi, Key-Sun and Chowdhury, Abdur (eds.) Proceedings of the 17th ACM Conference on Information and Knowledge Management - CIKM 2008 October 26-30, 2008, Napa Valley, California, USA. pp. 1439-1440.

 
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Zheng, Yu, Li, Quannan, Chen, Yukun, Xie, Xing and Ma, Wei-Ying (2008): Understanding mobility based on GPS data. In: Youn, Hee Yong and Cho, We-Duke (eds.) UbiComp 2008 Ubiquitous Computing - 10th International Conference September 21-24, 2008, Seoul, Korea. pp. 312-321.

 
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Wu, Lei, Hua, Xian-Sheng, Yu, Nenghai, Ma, Wei-Ying and Li, Shipeng (2008): Flickr distance. In: El-Saddik, Abdulmotaleb, Vuong, Son, Griwodz, Carsten, Bimbo, Alberto Del, Candan, K. Selcuk and Jaimes, Alejandro (eds.) Proceedings of the 16th International Conference on Multimedia 2008 October 26-31, 2008, Vancouver, British Columbia, Canada. pp. 31-40.

 
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Li, Zhiwei, Zhang, Lei and Ma, Wei-Ying (2008): Delivering online advertisements inside images. In: El-Saddik, Abdulmotaleb, Vuong, Son, Griwodz, Carsten, Bimbo, Alberto Del, Candan, K. Selcuk and Jaimes, Alejandro (eds.) Proceedings of the 16th International Conference on Multimedia 2008 October 26-31, 2008, Vancouver, British Columbia, Canada. pp. 1051-1060.

 
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Chang, Edward, Ong, Ken, Boll, Susanne and Ma, Wei-Ying (2008): Rich media and web 2.0. In: Proceedings of the 2008 International Conference on the World Wide Web 2008. pp. 1259-1260.

Rich media data, such as video, imagery, music, and gaming, do no longer play just a supporting role on the World Wide Web to text data. Thanks to Web 2.0, rich media is the primary content on sites such as Flickr, PicasaWeb, YouTube, and QQ. Because of massive user generated content, the volume of rich media being transmitted on the Internet has surpassed that of text. It is vital to properly manage these data to ensure efficient bandwidth utilization, to support effective indexing and search, and to safeguard copyrights (just to name a few). This panel invites both researchers and practitioners to discuss the challenges of Web-scale media-data management. In particular, the panelists will address issues such as leveraging Rich Media and Web 2.0, indexing, search, and scalability.

© All rights reserved Chang et al. and/or ACM Press

2007
 
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Nie, Zaiqing, Ma, Yunxiao, Shi, Shuming, Wen, Ji-Rong and Ma, Wei-Ying (2007): Web object retrieval. In: Proceedings of the 2007 International Conference on the World Wide Web 2007. pp. 81-90.

The primary function of current Web search engines is essentially relevance ranking at the document level. However, myriad structured information about real-world objects is embedded in static Web pages and online Web databases. Document-level information retrieval can unfortunately lead to highly inaccurate relevance ranking in answering object-oriented queries. In this paper, we propose a paradigm shift to enable searching at the object level. In traditional information retrieval models, documents are taken as the retrieval units and the content of a document is considered reliable. However, this reliability assumption is no longer valid in the object retrieval context when multiple copies of information about the same object typically exist. These copies may be inconsistent because of diversity of Web site qualities and the limited performance of current information extraction techniques. If we simply combine the noisy and inaccurate attribute information extracted from different sources, we may not be able to achieve satisfactory retrieval performance. In this paper, we propose several language models for Web object retrieval, namely an unstructured object retrieval model, a structured object retrieval model, and a hybrid model with both structured and unstructured retrieval features. We test these models on a paper search engine and compare their performances. We conclude that the hybrid model is the superior by taking into account the extraction errors at varying levels.

© All rights reserved Nie et al. and/or International World Wide Web Conference Committee

 
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Tsai, Ming-Feng, Liu, Tie-Yan, Qin, Tao, Chen, Hsinchun and Ma, Wei-Ying (2007): FRank: a ranking method with fidelity loss. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2007. pp. 383-390.

Ranking problem is becoming important in many fields, especially in information retrieval (IR). Many machine learning techniques have been proposed for ranking problem, such as RankSVM, RankBoost, and RankNet. Among them, RankNet, which is based on a probabilistic ranking framework, is leading to promising results and has been applied to a commercial Web search engine. In this paper we conduct further study on the probabilistic ranking framework and provide a novel loss function named fidelity loss for measuring loss of ranking. The fidelity loss not only inherits effective properties of the probabilistic ranking framework in RankNet, but possesses new properties that are helpful for ranking. This includes the fidelity loss obtaining zero for each document pair, and having a finite upper bound that is necessary for conducting query-level normalization. We also propose an algorithm named FRank based on a generalized additive model for the sake of minimizing the fidelity loss and learning an effective ranking function. We evaluated the proposed algorithm for two datasets: TREC dataset and real Web search dataset. The experimental results show that the proposed FRank algorithm outperforms other learning-based ranking methods on both conventional IR problem and Web search.

© All rights reserved Tsai et al. and/or ACM Press

 
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Rui, Xiaoguang, Li, Mingjing, Li, Zhiwei, Ma, Wei-Ying and Yu, Nenghai (2007): Bipartite graph reinforcement model for web image annotation. In: Lienhart, Rainer, Prasad, Anand R., Hanjalic, Alan, Choi, Sunghyun, Bailey, Brian P. and Sebe, Nicu (eds.) Proceedings of the 15th International Conference on Multimedia 2007 September 24-29, 2007, Augsburg, Germany. pp. 585-594.

 
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Liu, Jing, Wang, Bin, Li, Mingjing, Li, Zhiwei, Ma, Wei-Ying, Lu, Hanqing and Ma, Songde (2007): Dual cross-media relevance model for image annotation. In: Lienhart, Rainer, Prasad, Anand R., Hanjalic, Alan, Choi, Sunghyun, Bailey, Brian P. and Sebe, Nicu (eds.) Proceedings of the 15th International Conference on Multimedia 2007 September 24-29, 2007, Augsburg, Germany. pp. 605-614.

 
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Cai, Rui, Zhang, Chao, 0002, Chong Wang, Zhang, Lei and Ma, Wei-Ying (2007): MusicSense: contextual music recommendation using emotional allocation modeling. In: Lienhart, Rainer, Prasad, Anand R., Hanjalic, Alan, Choi, Sunghyun, Bailey, Brian P. and Sebe, Nicu (eds.) Proceedings of the 15th International Conference on Multimedia 2007 September 24-29, 2007, Augsburg, Germany. pp. 553-556.

 
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Cai, Rui, Zhang, Chao, Zhang, Lei and Ma, Wei-Ying (2007): Scalable music recommendation by search. In: Lienhart, Rainer, Prasad, Anand R., Hanjalic, Alan, Choi, Sunghyun, Bailey, Brian P. and Sebe, Nicu (eds.) Proceedings of the 15th International Conference on Multimedia 2007 September 24-29, 2007, Augsburg, Germany. pp. 1065-1074.

 
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Liu, Tie-Yan, Yang, Huai-Yuan, Zheng, Xin, Qin, Tao and Ma, Wei-Ying (2007): Fast Large-Scale Spectral Clustering by Sequential Shrinkage Optimization. In: Amati, Giambattista, Carpineto, Claudio and Romano, Giovanni (eds.) Advances in Information Retrieva - 29th European Conference on IR Research - ECIR 2007 April 2-5, 2007, Rome, Italy. pp. 319-330.

 
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Yu, Qing, Shi, Shuming, Li, Zhiwei, Wen, Ji-Rong and Ma, Wei-Ying (2007): Improve Ranking by Using Image Information. In: Amati, Giambattista, Carpineto, Claudio and Romano, Giovanni (eds.) Advances in Information Retrieva - 29th European Conference on IR Research - ECIR 2007 April 2-5, 2007, Rome, Italy. pp. 645-652.

2006
 
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Feng, Guang, Liu, Tie-Yan, Wang, Ying, Bao, Ying, Ma, Zhiming, Zhang, Xu-Dong and Ma, Wei-Ying (2006): AggregateRank: bringing order to web sites. In: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2006. pp. 75-82.

Since the website is one of the most important organizational structures of the Web, how to effectively rank websites has been essential to many Web applications, such as Web search and crawling. In order to get the ranks of websites, researchers used to describe the inter-connectivity among websites with a so-called HostGraph in which the nodes denote websites and the edges denote linkages between websites (if and only if there are hyperlinks from the pages in one website to the pages in the other, there will be an edge between these two websites), and then adopted the random walk model in the HostGraph. However, as pointed in this paper, the random walk over such a HostGraph is not reasonable because it is not in accordance with the browsing behavior of web surfers. Therefore, the derivate rank cannot represent the true probability of visiting the corresponding website. In this work, we mathematically proved that the probability of visiting a website by the random web surfer should be equal to the sum of the PageRank values of the pages inside that website. Nevertheless, since the number of web pages is much larger than that of websites, it is not feasible to base the calculation of the ranks of websites on the calculation of PageRank. To tackle this problem, we proposed a novel method named AggregateRank rooted in the theory of stochastic complement, which cannot only approximate the sum of PageRank accurately, but also have a lower computational complexity than PageRank. Both theoretical analysis and experimental evaluation show that AggregateRank is a better method for ranking websites than previous methods.

© All rights reserved Feng et al. and/or ACM Press

 
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Xiao, Xiangye, Luo, Qiong, Xie, Xing and Ma, Wei-Ying (2006): A comparative study on classifying the functions of web page blocks. In: Yu, Philip S., Tsotras, Vassilis J., Fox, Edward A. and Liu, Bing (eds.) Proceedings of the 2006 ACM CIKM International Conference on Information and Knowledge Management November 6-11, 2006, Arlington, Virginia, USA. pp. 776-777.

 
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Chen, Le, Zhang, Lei, Jing, Feng, Deng, Kefeng and Ma, Wei-Ying (2006): Ranking web objects from multiple communities. In: Yu, Philip S., Tsotras, Vassilis J., Fox, Edward A. and Liu, Bing (eds.) Proceedings of the 2006 ACM CIKM International Conference on Information and Knowledge Management November 6-11, 2006, Arlington, Virginia, USA. pp. 377-386.

 
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Jing, Feng, Wang, Changhu, Yao, Yuhuan, Deng, Kefeng, Zhang, Lei and Ma, Wei-Ying (2006): IGroup: web image search results clustering. In: Nahrstedt, Klara, Turk, Matthew, Rui, Yong, Klas, Wolfgang and Mayer-Patel, Ketan (eds.) Proceedings of the 14th ACM International Conference on Multimedia October 23-27, 2006, Santa Barbara, CA, USA. pp. 377-384.

 
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Li, Xirong, Chen, Le, Zhang, Lei, Lin, Fuzong and Ma, Wei-Ying (2006): Image annotation by large-scale content-based image retrieval. In: Nahrstedt, Klara, Turk, Matthew, Rui, Yong, Klas, Wolfgang and Mayer-Patel, Ketan (eds.) Proceedings of the 14th ACM International Conference on Multimedia October 23-27, 2006, Santa Barbara, CA, USA. pp. 607-610.

 
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Jing, Feng, Zhang, Lei and Ma, Wei-Ying (2006): VirtualTour: an online travel assistant based on high quality images. In: Nahrstedt, Klara, Turk, Matthew, Rui, Yong, Klas, Wolfgang and Mayer-Patel, Ketan (eds.) Proceedings of the 14th ACM International Conference on Multimedia October 23-27, 2006, Santa Barbara, CA, USA. pp. 599-602.

 
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Jing, Feng, Wang, Changhu, Yao, Yuhuan, Deng, Kefeng, Zhang, Lei and Ma, Wei-Ying (2006): IGroup: a web image search engine with semantic clustering of search results. In: Nahrstedt, Klara, Turk, Matthew, Rui, Yong, Klas, Wolfgang and Mayer-Patel, Ketan (eds.) Proceedings of the 14th ACM International Conference on Multimedia October 23-27, 2006, Santa Barbara, CA, USA. pp. 497-498.

 
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Zhang, Lei, Chen, Le, Jing, Feng, Deng, Kefeng and Ma, Wei-Ying (2006): EnjoyPhoto: a vertical image search engine for enjoying high-quality photos. In: Nahrstedt, Klara, Turk, Matthew, Rui, Yong, Klas, Wolfgang and Mayer-Patel, Ketan (eds.) Proceedings of the 14th ACM International Conference on Multimedia October 23-27, 2006, Santa Barbara, CA, USA. pp. 367-376.

 
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Lai, Wei, Hua, Xian-Sheng and Ma, Wei-Ying (2006): Towards content-based relevance ranking for video search. In: Nahrstedt, Klara, Turk, Matthew, Rui, Yong, Klas, Wolfgang and Mayer-Patel, Ketan (eds.) Proceedings of the 14th ACM International Conference on Multimedia October 23-27, 2006, Santa Barbara, CA, USA. pp. 627-630.

 
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Zhao, Qiankun, Hoi, Steven C. H., Liu, Tie-Yan, Bhowmick, Sourav S., Lyu, Michael R. and Ma, Wei-Ying (2006): Time-dependent semantic similarity measure of queries using historical click-through data. In: Proceedings of the 2006 International Conference on the World Wide Web 2006. pp. 543-552.

It has become a promising direction to measure similarity of Web search queries by mining the increasing amount of click-through data logged by Web search engines, which record the interactions between users and the search engines. Most existing approaches employ the click-through data for similarity measure of queries with little consideration of the temporal factor, while the click-through data is often dynamic and contains rich temporal information. In this paper we present a new framework of time-dependent query semantic similarity model on exploiting the temporal characteristics of historical click-through data. The intuition is that more accurate semantic similarity values between queries can be obtained by taking into account the timestamps of the log data. With a set of user-defined calendar schema and calendar patterns, our time-dependent query similarity model is constructed using the marginalized kernel technique, which can exploit both explicit similarity and implicit semantics from the click-through data effectively. Experimental results on a large set of click-through data acquired from a commercial search engine show that our time-dependent query similarity model is more accurate than the existing approaches. Moreover, we observe that our time-dependent query similarity model can, to some extent, reflect real-world semantics such as real-world events that are happening over time.

© All rights reserved Zhao et al. and/or ACM Press

 
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Wang, Xin-Jing, Zhang, Lei, Jing, Feng and Ma, Wei-Ying (2006): Image annotation using search and mining technologies. In: Proceedings of the 2006 International Conference on the World Wide Web 2006. pp. 1045-1046.

In this paper, we present a novel solution to the image annotation problem which annotates images using search and data mining technologies. An accurate keyword is required to initialize this process, and then leveraging a large-scale image database, it 1) searches for semantically and visually similar images, 2) and mines annotations from them. A notable advantage of this approach is that it enables unlimited vocabulary, while it is not possible for all existing approaches. Experimental results on real web images show the effectiveness and efficiency of the proposed algorithm.

© All rights reserved Wang et al. and/or ACM Press

 
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Song, Ruihua, Xin, Guomao, Shi, Shuming, Wen, Ji-Rong and Ma, Wei-Ying (2006): Exploring URL Hit Priors for Web Search. In: Lalmas, Mounia, MacFarlane, Andy, Rüger, Stefan M., Tombros, Anastasios, Tsikrika, Theodora and Yavlinsky, Alexei (eds.) Advances in Information Retrieval - 28th European Conference on IR Research - ECIR 2006 April 10-12, 2006, London, UK. pp. 277-288.

 
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Yao, Jinyi, Wang, Jue, Li, Zhiwei, Li, Mingjing and Ma, Wei-Ying (2006): Ranking Web News Via Homepage Visual Layout and Cross-Site Voting. In: Lalmas, Mounia, MacFarlane, Andy, Rüger, Stefan M., Tombros, Anastasios, Tsikrika, Theodora and Yavlinsky, Alexei (eds.) Advances in Information Retrieval - 28th European Conference on IR Research - ECIR 2006 April 10-12, 2006, London, UK. pp. 131-142.

2005
 
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Li, Zhiwei, Wang, Bin, Li, Mingjing and Ma, Wei-Ying (2005): A probabilistic model for retrospective news event detection. In: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2005. pp. 106-113.

Retrospective news event detection (RED) is defined as the discovery of previously unidentified events in historical news corpus. Although both the contents and time information of news articles are helpful to RED, most researches focus on the utilization of the contents of news articles. Few research works have been carried out on finding better usages of time information. In this paper, we do some explorations on both directions based on the following two characteristics of news articles. On the one hand, news articles are always aroused by events; on the other hand, similar articles reporting the same event often redundantly appear on many news sources. The former hints a generative model of news articles, and the latter provides data enriched environments to perform RED. With consideration of these characteristics, we propose a probabilistic model to incorporate both content and time information in a unified framework. This model gives new representations of both news articles and news events. Furthermore, based on this approach, we build an interactive RED system, HISCOVERY, which provides additional functions to present events, Photo Story and Chronicle.

© All rights reserved Li et al. and/or ACM Press

 
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Yan, Jun, Liu, Ning, Zhang, Benyu, Yan, Shuicheng, Chen, Zheng, Cheng, Qiansheng, Fan, Weiguo and Ma, Wei-Ying (2005): OCFS: optimal orthogonal centroid feature selection for text categorization. In: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2005. pp. 122-129.

Text categorization is an important research area in many Information Retrieval (IR) applications. To save the storage space and computation time in text categorization, efficient and effective algorithms for reducing the data before analysis are highly desired. Traditional techniques for this purpose can generally be classified into feature extraction and feature selection. Because of efficiency, the latter is more suitable for text data such as web documents. However, many popular feature selection techniques such as Information Gain (IG) and?2-test (CHI) are all greedy in nature and thus may not be optimal according to some criterion. Moreover, the performance of these greedy methods may be deteriorated when the reserved data dimension is extremely low. In this paper, we propose an efficient optimal feature selection algorithm by optimizing the objective function of Orthogonal Centroid (OC) subspace learning algorithm in a discrete solution space, called Orthogonal Centroid Feature Selection (OCFS). Experiments on 20 Newsgroups (20NG), Reuters Corpus Volume 1 (RCV1) and Open Directory Project (ODP) data show that OCFS is consistently better than IG and CHI with smaller computation time especially when the reduced dimension is extremely small.

© All rights reserved Yan et al. and/or ACM Press

 
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Qin, Tao, Liu, Tie-Yan, Zhang, Xu-Dong, Chen, Zheng and Ma, Wei-Ying (2005): A study of relevance propagation for web search. In: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2005. pp. 408-415.

Different from traditional information retrieval, both content and structure are critical to the success of Web information retrieval. In recent years, many relevance propagation techniques have been proposed to propagate content information between web pages through web structure to improve the performance of web search. In this paper, we first propose a generic relevance propagation framework, and then provide a comparison study on the effectiveness and efficiency of various representative propagation models that can be derived from this generic framework. We come to many conclusions that are useful for selecting a propagation model in real-world search applications, including 1) sitemap-based propagation models outperform hyperlink-based models in sense of both effectiveness and efficiency, and 2) sitemap-based term propagation is easier to be integrated into real-world search engines because of its parallel offline implementation and acceptable complexity. Some other more detailed study results are also reported in the paper.

© All rights reserved Qin et al. and/or ACM Press

 
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Wang, Lee, Wang, Chuang, Xie, Xing, Forman, Josh, Lu, Yansheng, Ma, Wei-Ying and Li, Ying (2005): Detecting dominant locations from search queries. In: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2005. pp. 424-431.

Accurately and effectively detecting the locations where search queries are truly about has huge potential impact on increasing search relevance. In this paper, we define a search query's dominant location (QDL) and propose a solution to correctly detect it. QDL is geographical location(s) associated with a query in collective human knowledge, i.e., one or few prominent locations agreed by majority of people who know the answer to the query. QDL is a subjective and collective attribute of search queries and we are able to detect QDLs from both queries containing geographical location names and queries not containing them. The key challenges to QDL detection include false positive suppression (not all contained location names in queries mean geographical locations), and detecting implied locations by the context of the query. In our solution, a query is recursively broken into atomic tokens according to its most popular web usage for reducing false positives. If we do not find a dominant location in this step, we mine the top search results and/or query logs (with different approaches discussed in this paper) to discover implicit query locations. Our large-scale experiments on recent MSN Search queries show that our query location detection solution has consistent high accuracy for all query frequency ranges.

© All rights reserved Wang et al. and/or ACM Press

 
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Shi, Shuming, Wen, Ji-Rong, Yu, Qing, Song, Ruihua and Ma, Wei-Ying (2005): Gravitation-based model for information retrieval. In: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2005. pp. 488-495.

This paper proposes GBM (gravitation-based model), a physical model for information retrieval inspired by Newton's theory of gravitation. A mapping is built in this model from concepts of information retrieval (documents, queries, relevance, etc) to those of physics (mass, distance, radius, attractive force, etc). This model actually provides a new perspective on IR problems. A family of effective term weighting functions can be derived from it, including the well-known BM25 formula. This model has some advantages over most existing ones: First, because it is directly based on basic physical laws, the derived formulas and algorithms can have their explicit physical interpretation. Second, the ranking formulas derived from this model satisfy more intuitive heuristics than most of existing ones, thus have the potential to behave empirically better and to be used safely on various settings. Finally, a new approach for structured document retrieval derived from this model is more reasonable and behaves better than existing ones.

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Zhang, Benyu, Li, Hua, Liu, Yi, Ji, Lei, Xi, Wensi, Fan, Weiguo, Chen, Zheng and Ma, Wei-Ying (2005): Improving web search results using affinity graph. In: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2005. pp. 504-511.

In this paper, we propose a novel ranking scheme named Affinity Ranking (AR) to re-rank search results by optimizing two metrics: (1) diversity -- which indicates the variance of topics in a group of documents; (2) information richness -- which measures the coverage of a single document to its topic. Both of the two metrics are calculated from a directed link graph named Affinity Graph (AG). AG models the structure of a group of documents based on the asymmetric content similarities between each pair of documents. Experimental results in Yahoo! Directory, ODP Data, and Newsgroup data demonstrate that our proposed ranking algorithm significantly improves the search performance. Specifically, the algorithm achieves 31% improvement in diversity and 12% improvement in information richness relatively within the top 10 search results.

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Xie, Xing, Liu, Hao, Goumaz, Simon and Ma, Wei-Ying (2005): Learning user interest for image browsing on small-form-factor devices. In: Proceedings of ACM CHI 2005 Conference on Human Factors in Computing Systems 2005. pp. 671-680.

Mobile devices which can capture and view pictures are becoming increasingly common in our life. The limitation of these small-form-factor devices makes the user experience of image browsing quite different from that on desktop PCs. In this paper, we first present a user study on how users interact with a mobile image browser with basic functions. We found that on small displays, users tend to use more zooming and scrolling actions in order to view interesting regions in detail. From this fact, we designed a new method to detect user interest maps and extract user attention objects from the image browsing log. This approach is more efficient than image-analysis based methods and can better represent users' actual interest. A smart image viewer was then developed based on user interest analysis. A second experiment was carried out to study how users behave with such a viewer. Experimental results demonstrate that the new smart features can improve the browsing efficiency and are a good compliment to traditional image browsers.

© All rights reserved Xie et al. and/or ACM Press

 
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Hua, Zhigang, Wang, Xiang-Jun, Xie, Xing, Liu, Qingshan, Lu, Hanqing and Ma, Wei-Ying (2005): AIRE: an ambient interactive and responsive environment for mobile image management. In: Proceedings of 7th conference on Human-computer interaction with mobile devices and services 2005. pp. 343-344.

This paper proposes an ambient interactive and responsive environment (AIRE) to improve user's image browsing experiences on the small-form-factor devices. This solution is characterized as two aspects: (1) establishing an ambient interactive communication across various devices; and (2) designing a distributed interface that crosses various devices to overcome the display constraint in mobile devices. In the AIRE system, a two-level image browsing scheme is designed to meet users' various image browsing needs.

© All rights reserved Hua et al. and/or ACM Press

 
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Hua, Zhigang, Wang, Xiang-Jun, Xie, Xing, Liu, Qingshan, Lu, Hanqing and Ma, Wei-Ying (2005): AIRE: an ambient interactive and responsive environment for mobile image management. In: Tscheligi, Manfred, Bernhaupt, Regina and Mihalic, Kristijan (eds.) Proceedings of the 7th Conference on Human-Computer Interaction with Mobile Devices and Services - Mobile HCI 2005 September 19-22, 2005, Salzburg, Austria. pp. 343-344.

 
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Zhou, Yinghua, Xie, Xing, Wang, Chuang, Gong, Yuchang and Ma, Wei-Ying (2005): Hybrid index structures for location-based web search. In: Herzog, Otthein, Schek, Hans-Jörg and Fuhr, Norbert (eds.) Proceedings of the 2005 ACM CIKM International Conference on Information and Knowledge Management October 31 - November 5, 2005, Bremen, Germany. pp. 155-162.

 
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Liu, Tie-Yan and Ma, Wei-Ying (2005): Webpage Importance Analysis Using Conditional Markov Random Walk. In: Skowron, Andrzej, Agrawal, Rakesh, Luck, Michael, Yamaguchi, Takahira, Morizet-Mahoudeaux, Pierre, Liu, Jiming and Zhong, Ning (eds.) 2005 IEEE / WIC / ACM International Conference on Web Intelligence WI 2005 19-22 September, 2005, Compiegne, France. pp. 515-521.

 
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Liu, Tie-Yan, Wan, Hao and Ma, Wei-Ying (2005): An Editor Labeling Model for Training Set Expansion in Web Categorization. In: Skowron, Andrzej, Agrawal, Rakesh, Luck, Michael, Yamaguchi, Takahira, Morizet-Mahoudeaux, Pierre, Liu, Jiming and Zhong, Ning (eds.) 2005 IEEE / WIC / ACM International Conference on Web Intelligence WI 2005 19-22 September, 2005, Compiegne, France. pp. 165-171.

 
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Chen, Yu, Xie, Xing, Ma, Wei-Ying and Zhang, Hongjiang (2005): Adapting Web Pages for Small-Screen Devices. In IEEE Internet Computing, 9 (1) pp. 50-56.

 
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Gao, Bin, Liu, Tie-Yan, Qin, Tao, Zheng, Xin, Cheng, Qiansheng and Ma, Wei-Ying (2005): Web image clustering by consistent utilization of visual features and surrounding texts. In: Zhang, Hongjiang, Chua, Tat-Seng, Steinmetz, Ralf, Kankanhalli, Mohan S. and Wilcox, Lynn (eds.) Proceedings of the 13th ACM International Conference on Multimedia November 6-11, 2005, Singapore. pp. 112-121.

 
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Tong, Hanghang, He, Jingrui, Li, Mingjing, Zhang, Changshui and Ma, Wei-Ying (2005): Graph based multi-modality learning. In: Zhang, Hongjiang, Chua, Tat-Seng, Steinmetz, Ralf, Kankanhalli, Mohan S. and Wilcox, Lynn (eds.) Proceedings of the 13th ACM International Conference on Multimedia November 6-11, 2005, Singapore. pp. 862-871.

 
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Wang, Xin-Jing, Ma, Wei-Ying, Zhang, Lei and Li, Xing (2005): Iteratively clustering web images based on link and attribute reinforcements. In: Zhang, Hongjiang, Chua, Tat-Seng, Steinmetz, Ralf, Kankanhalli, Mohan S. and Wilcox, Lynn (eds.) Proceedings of the 13th ACM International Conference on Multimedia November 6-11, 2005, Singapore. pp. 122-131.

 
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Nie, Zaiqing, Zhang, Yuanzhi, Wen, Ji-Rong and Ma, Wei-Ying (2005): Object-level ranking: bringing order to Web objects. In: Proceedings of the 2005 International Conference on the World Wide Web 2005. pp. 567-574.

In contrast with the current Web search methods that essentially do document-level ranking and retrieval, we are exploring a new paradigm to enable Web search at the object level. We collect Web information for objects relevant for a specific application domain and rank these objects in terms of their relevance and popularity to answer user queries. Traditional PageRank model is no longer valid for object popularity calculation because of the existence of heterogeneous relationships between objects. This paper introduces PopRank, a domain-independent object-level link analysis model to rank the objects within a specific domain. Specifically we assign a popularity propagation factor to each type of object relationship, study how different popularity propagation factors for these heterogeneous relationships could affect the popularity ranking, and propose efficient approaches to automatically decide these factors. Our experiments are done using 1 million CS papers, and the experimental results show that PopRank can achieve significantly better ranking results than naively applying PageRank on the object graph.

© All rights reserved Nie et al. and/or ACM Press

 
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Shahraray, Behzad, Ma, Wei-Ying, Zakhor, Avideh and Babaguchi, Noboru (2005): Mobile multimedia services. In: Proceedings of the 2005 International Conference on the World Wide Web 2005. p. 795.

This panel will mainly focus on the role that media processing can play in creating mobile communications, information, and entertainment services. A major premise of our discussion is that media processing techniques go beyond compression and can be employed to monitor, filter, convert, and repurpose information. Such automated techniques can serve to create personalized information and entertainment services in a cost-effective way, adapt existing content for consumption on mobile devices, and circumvent the inherent limitations of mobile devices. Some examples of the applications of media processing techniques for mobile service generation will be given.

© All rights reserved Shahraray et al. and/or ACM Press

 
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Liu, Tie-Yan, Yang, Yiming, Wan, Hao, ZHOU, Qian, Gao, Bin, Zeng, Hua-Jun, Chen, Zheng and Ma, Wei-Ying (2005): An experimental study on large-scale web categorization. In: Proceedings of the 2005 International Conference on the World Wide Web 2005. pp. 1106-1107.

Taxonomies of the Web typically have hundreds of thousands of categories and skewed category distribution over documents. It is not clear whether existing text classification technologies can perform well on and scale up to such large-scale applications. To understand this, we conducted the evaluation of several representative methods (Support Vector Machines, k-Nearest Neighbor and Naive Bayes) with Yahoo! taxonomies. In particular, we evaluated the effectiveness/efficiency tradeoff in classifiers with hierarchical setting compared to conventional (flat) setting, and tested popular threshold tuning strategies for their scalability and accuracy in large-scale classification problems.

© All rights reserved Liu et al. and/or ACM Press

 
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Liu, Tie-Yan, Wan, Hao, Qin, Tao, Chen, Zheng, REN, Yong and Ma, Wei-Ying (2005): Site abstraction for rare category classification in large-scale web directory. In: Proceedings of the 2005 International Conference on the World Wide Web 2005. pp. 1108-1109.

Automatically classifying the Web directories is an effective way to manage Web information. However, our experiments showed that the state-of-the-art text classification technologies could not lead to acceptable performance in this task. Due to our analysis, the main problem is the lack of effective training data in rare categories of Web directories. To tackle this problem, we proposed a novel technology named Site Abstraction to synthesize new training examples from the website of the existing training document. The main idea is to propagate features through parent-child relationship in the sitemap tree. Experiments showed that our method significantly improved the classification performance.

© All rights reserved Liu et al. and/or ACM Press

 
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Wang, Chuang, Xie, Xing, Wang, Lee, Lu, Yansheng and Ma, Wei-Ying (2005): Web resource geographic location classification and detection. In: Proceedings of the 2005 International Conference on the World Wide Web 2005. pp. 1138-1139.

Rapid pervasion of the web into users' daily lives has put much importance on capturing location-specific information on the web, due to the fact that most human activities occur locally around where a user is located. This is especially true in the increasingly popular mobile and local search environments. Thus, how to correctly and effectively detect locations from web resources has become a key challenge to location-based web applications. In this paper, we first explicitly distinguish the locations of web resources into three types to cater to different application needs: 1) provider location; 2) content location; and 3) serving location. Then we describe a unified system that computes each of the three locations, employing a set of algorithms and different geographic sources.

© All rights reserved Wang et al. and/or ACM Press

 
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Hua, Zhigang, Liu, Hao, Xie, Xing, Lu, Hanqing and Ma, Wei-Ying (2005): Representing personal web information using a topic-oriented interface. In: Proceedings of the 2005 International Conference on the World Wide Web 2005. pp. 1142-1143.

Nowadays, Web activities have become daily practice for people. It is therefore essential to organize and present this continuously increasing Web information in a more usable manner. In this paper, we developed a novel approach to reorganize personal Web information as a topic-oriented interface. In our approach, we proposed to utilize anchor, title and URL information to represent content information for the browsed Web pages rather than the content body. Furthermore, we explored three methods to organize personal Web information: 1) top-down statistical clustering; 2) salience phrase based clustering; and 3) support vector machine (SVM) based classification. Finally, we conducted a usability study to verify the effectiveness of our proposed solution. The experimental results demonstrated that users could visit the pages that have been browsed previously more easily with our approach than existing solutions.

© All rights reserved Hua et al. and/or ACM Press

 
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Xie, Xing, Miao, Gengxin, Song, Ruihua, Wen, Ji-Rong and Ma, Wei-Ying (2005): Efficient Browsing of Web Search Results on Mobile Devices Based on Block Importance Model. In: PerCom 2005 - 3rd IEEE International Conference on Pervasive Computing and Communications 8-12 March, 2005, Kauai Island, HI, USA. pp. 17-26.

2004
 
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Baudisch, Patrick, Xie, Xing, Wang, Chong and Ma, Wei-Ying (2004): Collapse-to-zoom: viewing web pages on small screen devices by interactively removing irrelevant content. In: Proceedings of the 2004 ACM Symposium on User Interface Software and Technology 2004. pp. 91-94.

Overview visualizations for small-screen web browsers were designed to provide users with visual context and to allow them to rapidly zoom in on tiles of relevant content. Given that content in the overview is reduced, however, users are often unable to tell which tiles hold the relevant material, which can force them to adopt a time-consuming hunt-and-peck strategy. Collapse-to-zoom addresses this issue by offering an alternative exploration strategy. In addition to allowing users to zoom into relevant areas, collapse-to-zoom allows users to collapse areas deemed irrelevant, such as columns containing menus, archive material, or advertising. Collapsing content causes all remaining content to expand in size causing it to reveal more detail, which increases the user\'s chance of identifying relevant content. Collapse-to-zoom navigation is based on a hybrid between a marquee selection tool and a marking menu, called marquee menu. It offers four commands for collapsing content areas at different granularities and to switch to a full-size reading view of what is left of the page.

© All rights reserved Baudisch et al. and/or ACM Press

 
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Wen, Ji-Rong, Lao, Ni and Ma, Wei-Ying (2004): Probabilistic model for contextual retrieval. In: Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2004. pp. 57-63.

Contextual retrieval is a critical technique for facilitating many important applications such as mobile search, personalized search, PC troubleshooting, etc. Despite of its importance, there is no comprehensive retrieval model to describe the contextual retrieval process. We observed that incompatible context, noisy context and incomplete query are several important issues commonly existing in contextual retrieval applications. However, these issues have not been previously explored and discussed. In this paper, we propose probabilistic models to address these problems. Our study clearly shows that query log is the key to build effective contextual retrieval models. We also conduct a case study in the PC troubleshooting domain to testify the performance of the proposed models and experimental results show that the models can achieve very good retrieval precision.

© All rights reserved Wen et al. and/or ACM Press

 
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He, Xiaofei, Cai, Deng, Liu, Haifeng and Ma, Wei-Ying (2004): Locality preserving indexing for document representation. In: Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2004. pp. 96-103.

Document representation and indexing is a key problem for document analysis and processing, such as clustering, classification and retrieval. Conventionally, Latent Semantic Indexing (LSI) is considered effective in deriving such an indexing. LSI essentially detects the most representative features for document representation rather than the most discriminative features. Therefore, LSI might not be optimal in discriminating documents with different semantics. In this paper, a novel algorithm called Locality Preserving Indexing (LPI) is proposed for document indexing. Each document is represented by a vector with low dimensionality. In contrast to LSI which discovers the global structure of the document space, LPI discovers the local structure and obtains a compact document representation subspace that best detects the essential semantic structure. We compare the proposed LPI approach with LSI on two standard databases. Experimental results show that LPI provides better representation in the sense of semantic structure.

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Zeng, Hua-Jun, He, Qi-Cai, Chen, Zheng, Ma, Wei-Ying and Ma, Jinwen (2004): Learning to cluster web search results. In: Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2004. pp. 210-217.

Organizing Web search results into clusters facilitates users' quick browsing through search results. Traditional clustering techniques are inadequate since they don't generate clusters with highly readable names. In this paper, we reformalize the clustering problem as a salient phrase ranking problem. Given a query and the ranked list of documents (typically a list of titles and snippets) returned by a certain Web search engine, our method first extracts and ranks salient phrases as candidate cluster names, based on a regression model learned from human labeled training data. The documents are assigned to relevant salient phrases to form candidate clusters, and the final clusters are generated by merging these candidate clusters. Experimental results verify our method's feasibility and effectiveness.

© All rights reserved Zeng et al. and/or ACM Press

 
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Cai, Deng, He, Xiaofei, Wen, Ji-Rong and Ma, Wei-Ying (2004): Block-level link analysis. In: Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2004. pp. 440-447.

Link Analysis has shown great potential in improving the performance of web search. PageRank and HITS are two of the most popular algorithms. Most of the existing link analysis algorithms treat a web page as a single node in the web graph. However, in most cases, a web page contains multiple semantics and hence the web page might not be considered as the atomic node. In this paper, the web page is partitioned into blocks using the vision-based page segmentation algorithm. By extracting the page-to-block, block-to-page relationships from link structure and page layout analysis, we can construct a semantic graph over the WWW such that each node exactly represents a single semantic topic. This graph can better describe the semantic structure of the web. Based on block-level link analysis, we proposed two new algorithms, Block Level PageRank and Block Level HITS, whose performances we study extensively using web data.

© All rights reserved Cai et al. and/or ACM Press

 
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Cai, Deng, Yu, Shipeng, Wen, Ji-Rong and Ma, Wei-Ying (2004): Block-based web search. In: Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2004. pp. 456-463.

In this paper, we introduce an information theoretic method for estimating the usefulness of the hyperlink structure induced from the set of retrieved documents. We evaluate the effectiveness of this method in the context of an optimal Bayesian decision mechanism, which selects the most appropriate retrieval approaches on a per-query basis for two TREC tasks. The estimation of the hyperlink structure's usefulness is stable when we use different weighting schemes, or when we employ sampling of documents to reduce the computational overhead. Next, we evaluate the effectiveness of the hyperlink structure's usefulness in a realistic setting, by setting the thresholds of a decision mechanism automatically. Our results show that improvements over the baselines are obtained.

© All rights reserved Cai et al. and/or ACM Press

 
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Liu, Ning, Zhang, Benyu, Yan, Jun, Yang, Qiang, Yan, Shuicheng, Chen, Zheng, Bai, Fengshan and Ma, Wei-Ying (2004): Learning similarity measures in non-orthogonal space. In: Grossman, David A., Gravano, Luis, Zhai, Chengxiang, Herzog, Otthein and Evans, David A. (eds.) Proceedings of the 2004 ACM CIKM International Conference on Information and Knowledge Management November 8-13, 2004, Washington, DC, USA. pp. 334-341.

 
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Wang, Xuanhui, Shen, Dou, Zeng, Hua-Jun, Chen, Zheng and Ma, Wei-Ying (2004): Web page clustering enhanced by summarization. In: Grossman, David A., Gravano, Luis, Zhai, Chengxiang, Herzog, Otthein and Evans, David A. (eds.) Proceedings of the 2004 ACM CIKM International Conference on Information and Knowledge Management November 8-13, 2004, Washington, DC, USA. pp. 242-243.

 
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Xue, Gui-Rong, Zeng, Hua-Jun, Chen, Zheng, Yu, Yong, Ma, Wei-Ying, Xi, Wensi and Fan, Weiguo (2004): Optimizing web search using web click-through data. In: Grossman, David A., Gravano, Luis, Zhai, Chengxiang, Herzog, Otthein and Evans, David A. (eds.) Proceedings of the 2004 ACM CIKM International Conference on Information and Knowledge Management November 8-13, 2004, Washington, DC, USA. pp. 118-126.

 
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Xue, Gui-Rong, Zeng, Hua-Jun, Chen, Zheng, Yu, Yong, Ma, Wei-Ying, Xi, Wensi and Fox, Edward A. (2004): MRSSA: an iterative algorithm for similarity spreading over interrelated objects. In: Grossman, David A., Gravano, Luis, Zhai, Chengxiang, Herzog, Otthein and Evans, David A. (eds.) Proceedings of the 2004 ACM CIKM International Conference on Information and Knowledge Management November 8-13, 2004, Washington, DC, USA. pp. 240-241.

 
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Huang, Shen, Xue, Gui-Rong, Zhang, Benyu, Chen, Zheng, Yu, Yong and Ma, Wei-Ying (2004): TSSP: A Reinforcement Algorithm to Find Related Papers. In: 2004 IEEE/WIC/ACM International Conference on Web Intelligence WI 2004 20-24 September, 2004, Beijing, China. pp. 117-123.

 
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Sun, Jian-Tao, Zhang, Benyu, Chen, Zheng, Lu, Yuchang, Shi, Chunyi and Ma, Wei-Ying (2004): GE-CKO: A Method to Optimize Composite Kernels for Web Page Classification. In: 2004 IEEE/WIC/ACM International Conference on Web Intelligence WI 2004 20-24 September, 2004, Beijing, China. pp. 299-305.

 
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Cai, Deng, He, Xiaofei, Li, Zhiwei, Ma, Wei-Ying and Wen, Ji-Rong (2004): Hierarchical clustering of WWW image search results using visual, textual and link information. In: Schulzrinne, Henning, Dimitrova, Nevenka, Sasse, Martina Angela, Moon, Sue B. and Lienhart, Rainer (eds.) Proceedings of the 12th ACM International Conference on Multimedia October 10-16, 2004, New York, NY, USA. pp. 952-959.

 
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He, Xiaofei, Ma, Wei-Ying and Zhang, Hongjiang (2004): Learning an image manifold for retrieval. In: Schulzrinne, Henning, Dimitrova, Nevenka, Sasse, Martina Angela, Moon, Sue B. and Lienhart, Rainer (eds.) Proceedings of the 12th ACM International Conference on Multimedia October 10-16, 2004, New York, NY, USA. pp. 17-23.

 
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Li, Zhiwei, Xie, Xing, Liu, Hao, Tang, Xiaoou, Li, Mingjing and Ma, Wei-Ying (2004): Intuitive and effective interfaces for WWW image search engines. In: Schulzrinne, Henning, Dimitrova, Nevenka, Sasse, Martina Angela, Moon, Sue B. and Lienhart, Rainer (eds.) Proceedings of the 12th ACM International Conference on Multimedia October 10-16, 2004, New York, NY, USA. pp. 748-749.

 
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Wang, Xin-Jing, Ma, Wei-Ying, He, Qi-Cai and Li, Xing (2004): Grouping web image search result. In: Schulzrinne, Henning, Dimitrova, Nevenka, Sasse, Martina Angela, Moon, Sue B. and Lienhart, Rainer (eds.) Proceedings of the 12th ACM International Conference on Multimedia October 10-16, 2004, New York, NY, USA. pp. 436-439.

 
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Wang, Xin-Jing, Ma, Wei-Ying, Xue, Gui-Rong and Li, Xing (2004): Multi-model similarity propagation and its application for web image retrieval. In: Schulzrinne, Henning, Dimitrova, Nevenka, Sasse, Martina Angela, Moon, Sue B. and Lienhart, Rainer (eds.) Proceedings of the 12th ACM International Conference on Multimedia October 10-16, 2004, New York, NY, USA. pp. 944-951.

 
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Zhang, Lei, Hu, Yuxiao, Li, Mingjing, Ma, Wei-Ying and Zhang, Hongjiang (2004): Efficient propagation for face annotation in family albums. In: Schulzrinne, Henning, Dimitrova, Nevenka, Sasse, Martina Angela, Moon, Sue B. and Lienhart, Rainer (eds.) Proceedings of the 12th ACM International Conference on Multimedia October 10-16, 2004, New York, NY, USA. pp. 716-723.

 
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Zheng, Xin, Cai, Deng, He, Xiaofei, Ma, Wei-Ying and Lin, Xueyin (2004): Locality preserving clustering for image database. In: Schulzrinne, Henning, Dimitrova, Nevenka, Sasse, Martina Angela, Moon, Sue B. and Lienhart, Rainer (eds.) Proceedings of the 12th ACM International Conference on Multimedia October 10-16, 2004, New York, NY, USA. pp. 885-891.

 
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Song, Ruihua, Liu, Haifeng, Wen, Ji-Rong and Ma, Wei-Ying (2004): Learning block importance models for web pages. In: Proceedings of the 2004 International Conference on the World Wide Web 2004. pp. 203-211.

Previous work shows that a web page can be partitioned into multiple segments or blocks, and often the importance of those blocks in a page is not equivalent. Also, it has been proven that differentiating noisy or unimportant blocks from pages can facilitate web mining, search and accessibility. However, no uniform approach and model has been presented to measure the importance of different segments in web pages. Through a user study, we found that people do have a consistent view about the importance of blocks in web pages. In this paper, we investigate how to find a model to automatically assign importance values to blocks in a web page. We define the block importance estimation as a learning problem. First, we use a vision-based page segmentation algorithm to partition a web page into semantic blocks with a hierarchical structure. Then spatial features (such as position and size) and content features (such as the number of images and links) are extracted to construct a feature vector for each block. Based on these features, learning algorithms are used to train a model to assign importance to different segments in the web page. In our experiments, the best model can achieve the performance with Micro-F1 79% and

© All rights reserved Song et al. and/or ACM Press

 
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Xi, Wensi, Zhang, Benyu, Chen, Zheng, Lu, Yizhou, Yan, Shuicheng, Ma, Wei-Ying and Fox, Edward A. (2004): Link fusion: a unified link analysis framework for multi-type interrelated data objects. In: Proceedings of the 2004 International Conference on the World Wide Web 2004. pp. 319-327.

Web link analysis has proven to be a significant enhancement for quality based web search. Most existing links can be classified into two categories: intra-type links (e.g., web hyperlinks), which represent the relationship of data objects within a homogeneous data type (web pages), and inter-type links (e.g., user browsing log) which represent the relationship of data objects across different data types (users and web pages). Unfortunately, most link analysis research only considers one type of link. In this paper, we propose a unified link analysis framework, called "link fusion", which considers both the inter- and intra- type link structure among multiple-type inter-related data objects and brings order to objects in each data type at the same time. The PageRank and HITS algorithms are shown to be special cases of our unified link analysis framework. Experiments on an instantiation of the framework that makes use of the user data and web pages extracted from a proxy log show that our proposed algorithm could improve the search effectiveness over the HITS and DirectHit algorithms by 24.6% and 38.2% respectively.

© All rights reserved Xi et al. and/or ACM Press

 
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Lu, Yizhou, Zhang, Benyu, Xi, Wensi, Chen, Zheng, Liu, Yi, Lyu, Michael R. and Ma, Wei-Ying (2004): The PowerRank web link analysis algorithm. In: Proceedings of the 2004 International Conference on the World Wide Web 2004. pp. 254-255.

The web graph follows the power law distribution and has a hierarchy structure. But neither the PageRank algorithm nor any of its improvements leverage these attributes. In this paper, we propose a novel link analysis algorithm "the PowerRank algorithm", which makes use of the power law distribution attribute and the hierarchy structure of the web graph. The algorithm consists two parts. In the first part, special treatment is applied to the web pages with low "importance" score. In the second part, the global "importance" score for each web page is obtained by combining those scores together. Our experimental results show that: 1) The PowerRank algorithm computes 10%-30% faster than PageRank algorithm. 2) Top web pages in PowerRank algorithm remain similar to that of the PageRank algorithm.

© All rights reserved Lu et al. and/or ACM Press

 
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Xue, Gui-Rong, Zeng, Hua-Jun, Chen, Zheng, Ma, Wei-Ying and Yu, Yong (2004): Similarity spreading: a unified framework for similarity calculation of interrelated objects. In: Proceedings of the 2004 International Conference on the World Wide Web 2004. pp. 460-461.

In many Web search applications, similarities between objects of one type (say, queries) can be affected by the similarities between their interrelated objects of another type (say, Web pages), and vice versa. We propose a novel framework called similarity spreading to take account of the interrelationship and improve the similarity calculation. Experiment results show that the proposed framework can significantly improve the accuracy of the similarity measurement of the objects in a search engine.

© All rights reserved Xue et al. and/or ACM Press

2003
 
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Xue, Gui-Rong, Zeng, Hua-Jun, Chen, Zheng, Ma, Wei-Ying, Zhang, Hong-Jiang and Lu, Chao-Jun (2003): Implicit link analysis for small web search. In: Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2003. pp. 56-63.

Current Web search engines generally impose link analysis-based re-ranking on web-page retrieval. However, the same techniques, when applied directly to small web search such as intranet and site search, cannot achieve the same performance because their link structures are different from the global Web. In this paper, we propose an approach to constructing implicit links by mining users' access patterns, and then apply a modified PageRank algorithm to re-rank web-pages for small web search. Our experimental results indicate that the

© All rights reserved Xue et al. and/or ACM Press

 
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Wang, Jidong, Zeng, Huajun, Chen, Zheng, Lu, Hongjun, Tao, Li and Ma, Wei-Ying (2003): ReCoM: reinforcement clustering of multi-type interrelated data objects. In: Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2003. pp. 274-281.

Most existing clustering algorithms cluster highly related data objects such as Web pages and Web users separately. The interrelation among different types of data objects is either not considered, or represented by a static feature space and treated in the same ways as other attributes of the objects. In this paper, we propose a novel clustering approach for clustering multi-type interrelated data objects, ReCoM (Reinforcement Clustering of Multi-type Interrelated data objects). Under this approach, relationships among data objects are used to improve the cluster quality of interrelated data objects through an iterative reinforcement clustering process. At the same time, the link structure derived from relationships of the interrelated data objects is used to differentiate the importance of objects and the learned importance is also used in the clustering process to further improve the clustering results. Experimental results show that the proposed approach not only effectively overcomes the problem of data sparseness caused by the high dimensional relationship space but also significantly improves the clustering accuracy.

© All rights reserved Wang et al. and/or ACM Press

 
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Fan, Xin, Xie, Xing, Zhou, He-Qin and Ma, Wei-Ying (2003): Looking into video frames on small displays. In: Rowe, Lawrence A., Vin, Harrick M., Plagemann, Thomas, Shenoy, Prashant J. and Smith, John R. (eds.) Proceedings of the Eleventh ACM International Conference on Multimedia November 2-8, 2003, Berkeley, CA, USA. pp. 247-250.

 
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Liu, Hao, Xie, Xing, Ma, Wei-Ying and Zhang, Hongjiang (2003): Automatic browsing of large pictures on mobile devices. In: Rowe, Lawrence A., Vin, Harrick M., Plagemann, Thomas, Shenoy, Prashant J. and Smith, John R. (eds.) Proceedings of the Eleventh ACM International Conference on Multimedia November 2-8, 2003, Berkeley, CA, USA. pp. 148-155.

 
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Wang, Ming-Yu, Xie, Xing, Ma, Wei-Ying and Zhang, Hongjiang (2003): MobiPicture: browsing pictures on mobile devices. In: Rowe, Lawrence A., Vin, Harrick M., Plagemann, Thomas, Shenoy, Prashant J. and Smith, John R. (eds.) Proceedings of the Eleventh ACM International Conference on Multimedia November 2-8, 2003, Berkeley, CA, USA. pp. 106-107.

 
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Yu, Bin, Ma, Wei-Ying, Nahrstedt, Klara and Zhang, Hongjiang (2003): Video summarization based on user log enhanced link analysis. In: Rowe, Lawrence A., Vin, Harrick M., Plagemann, Thomas, Shenoy, Prashant J. and Smith, John R. (eds.) Proceedings of the Eleventh ACM International Conference on Multimedia November 2-8, 2003, Berkeley, CA, USA. pp. 382-391.

 
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Yu, Kai, Ma, Wei-Ying, Tresp, Volker, Xu, Zhao, He, Xiaofei, Zhang, Hongjiang and Kriegel, Hans-Peter (2003): Knowing a tree from the forest: art image retrieval using a society of profiles. In: Rowe, Lawrence A., Vin, Harrick M., Plagemann, Thomas, Shenoy, Prashant J. and Smith, John R. (eds.) Proceedings of the Eleventh ACM International Conference on Multimedia November 2-8, 2003, Berkeley, CA, USA. pp. 622-631.

 
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Yu, Shipeng, Cai, Deng, Wen, Ji-Rong and Ma, Wei-Ying (2003): Improving pseudo-relevance feedback in web information retrieval using web page segmentation. In: Proceedings of the 2003 International Conference on the World Wide Web 2003. pp. 11-18.

In contrast to traditional document retrieval, a web page as a whole is not a good information unit to search because it often contains multiple topics and a lot of irrelevant information from navigation, decoration, and interaction part of the page. In this paper, we propose a VIsion-based Page Segmentation (VIPS) algorithm to detect the semantic content structure in a web page. Compared with simple DOM based segmentation method, our page segmentation scheme utilizes useful visual cues to obtain a better partition of a page at the semantic level. By using our VIPS algorithm to assist the selection of query expansion terms in pseudo-relevance feedback in web information retrieval, we achieve 27% performance improvement on Web Track dataset.

© All rights reserved Yu et al. and/or ACM Press

 
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Chen, Yu, Ma, Wei-Ying and Zhang, Hong-Jiang (2003): Detecting web page structure for adaptive viewing on small form factor devices. In: Proceedings of the 2003 International Conference on the World Wide Web 2003. pp. 225-233.

Mobile devices have already been widely used to access the Web. However, because most available web pages are designed for desktop PC in mind, it is inconvenient to browse these large web pages on a mobile device with a small screen. In this paper, we propose a new browsing convention to facilitate navigation and reading on a small-form-factor device. A web page is organized into a two level hierarchy with a thumbnail representation at the top level for providing a global view and index to a set of sub-pages at the bottom level for detail information. A page adaptation technique is also developed to analyze the structure of an existing web page and split it into small and logically related units that fit into the screen of a mobile device. For a web page not suitable for splitting, auto-positioning or scrolling-by-block is used to assist the browsing as an alternative. Our experimental results show that our proposed browsing convention and developed page adaptation scheme greatly improve the user's browsing experiences on a device with a small display.

© All rights reserved Chen et al. and/or ACM Press

2002
 
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He, Xiaofei, Ma, Wei-Ying, King, Oliver, Li, Mingjing and Zhang, Hongjiang (2002): Learning and inferring a semantic space from user's relevance feedback for image retrieval. In: ACM Multimedia 2002 2002. pp. 343-346.

 
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Xie, Xing, Zeng, Hua-Jun and Ma, Wei-Ying (2002): Enabling personalization services on the edge. In: ACM Multimedia 2002 2002. pp. 263-266.

 
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Cui, Hang, Wen, Ji-Rong, Nie, Jian-Yun and Ma, Wei-Ying (2002): Probabilistic query expansion using query logs. In: Proceedings of the 2002 International Conference on the World Wide Web 2002. pp. 325-332.

Query expansion has long been suggested as an effective way to resolve the short query and word mismatching problems. A number of query expansion methods have been proposed in traditional information retrieval. However, these previous methods do not take into account the specific characteristics of web searching; in particular, of the availability of large amount of user interaction information recorded in the web query logs. In this study, we propose a new method for query expansion based on query logs. The central idea is to extract probabilistic correlations between query terms and document terms by analyzing query logs. These correlations are then used to select high-quality expansion terms for new queries. The experimental results show that our log-based probabilistic query expansion method can greatly improve the search performance and has several advantages over other existing methods.

© All rights reserved Cui et al. and/or ACM Press

1998
 
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Ma, Wei-Ying and Manjunath, B. S. (1998): A Texture Thesaurus for Browsing Large Aerial Photographs. In JASIST - Journal of the American Society for Information Science and Technology, 49 (7) pp. 633-648.

 
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Page Information

Page maintainer: The Editorial Team
URL: http://www.interaction-design.org/references/authors/wei-ying_ma.html

Publication statistics

Pub. period:1998-2011
Pub. count:95
Number of co-authors:155



Co-authors

Number of publications with 3 favourite co-authors:

Xing Xie:22
Lei Zhang:17
Zheng Chen:17

 

 

Productive colleagues

Wei-Ying Ma's 3 most productive colleagues in number of publications:

Hsinchun Chen:145
Edward A. Fox:109
Zheng Chen:62
 
 
 
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