Publication statistics

Pub. period:2002-2011
Pub. count:31
Number of co-authors:56



Co-authors

Number of publications with 3 favourite co-authors:

Wei-Ying Ma:22
Yu Zheng:8
Hao Liu:4

 

 

Productive colleagues

Xing Xie's 3 most productive colleagues in number of publications:

Wei-Ying Ma:95
Patrick Baudisch:57
Hongjiang Zhang:45
 
 
 

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Publications by Xing Xie (bibliography)

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2011
 
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Ye, Mao, Xiao, Rong, Lee, Wang-Chien and Xie, Xing (2011): Location relevance classification for travelogue digests. In: Proceedings of the 2011 International Conference on the World Wide Web 2011. pp. 163-164.

In this paper, we aim to develop a travelogue service to discover and convey various travelogue digests, in form of theme locations and geographical scope to their readers. In this service, theme locations in a travelogue are the core information to discover. Due to the inherent ambiguity of location relevance, we explore the textual (e.g., surrounding words) and geographical (e.g., geographical relationship among locations) features of locations to perform location relevance classification for theme location discovery. Finally, we conduct comprehensive experiments on collected travelogues to evaluate the performance of our location relevance classification technique and demonstrate the effectiveness of the travelogue service.

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

 
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Ye, Mao, Xiao, Rong, Lee, Wang-Chien and Xie, Xing (2011): On theme location discovery for travelogue services. In: Proceedings of the 34th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2011. pp. 465-474.

In this paper, we aim to develop a travelogue service that discovers and conveys various travelogue digests, in form of theme locations, geographical scope, traveling trajectory and location snippet, to users. In this service, theme locations in a travelogue are the core information to discover. Thus we aim to address the problem of theme location discovery to enable the above travelogue services. Due to the inherent ambiguity of location relevance, we perform location relevance mining (LRM) in two complementary angles, relevance classification and relevance ranking, to provide comprehensive understanding of locations. Furthermore, we explore the textual (e.g., surrounding words) and geographical (e.g., geographical relationship among locations) features of locations to develop a co-training model for enhancement of classification performance. Built upon the mining result of LRM, we develop a series of techniques for provisioning of the aforementioned travelogue digests in our travelogue system. Finally, we conduct comprehensive experiments on collected travelogues to evaluate the performance of our location relevance mining techniques and demonstrate the effectiveness of the travelogue service.

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

 
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Zheng, Yu, Liu, Yanchi, Yuan, Jing and Xie, Xing (2011): Urban computing with taxicabs. In: Proceedings of the 2011 International Conference on Uniquitous Computing 2011. pp. 89-98.

Urban computing for city planning is one of the most significant applications in Ubiquitous computing. In this paper we detect flawed urban planning using the GPS trajectories of taxicabs traveling in urban areas. The detected results consist of 1) pairs of regions with salient traffic problems and 2) the linking structure as well as correlation among them. These results can evaluate the effectiveness of the carried out planning, such as a newly built road and subway lines in a city, and remind city planners of a problem that has not been recognized when they conceive future plans. We conduct our method using the trajectories generated by 30,000 taxis from March to May in 2009 and 2010 in Beijing, and evaluate our results with the real urban planning of Beijing.

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

 
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Yuan, Jing, Zheng, Yu, Zhang, Liuhang, Xie, Xing and Sun, Guangzhong (2011): Where to find my next passenger. In: Proceedings of the 2011 International Conference on Uniquitous Computing 2011. pp. 109-118.

We present a recommender for taxi drivers and people expecting to take a taxi, using the knowledge of 1) passengers' mobility patterns and 2) taxi drivers' pick-up behaviors learned from the GPS trajectories of taxicabs. First, this recommender provides taxi drivers with some locations and the routes to these locations, towards which they are more likely to pick up passengers quickly (during the routes or at these locations) and maximize the profit. Second, it recommends people with some locations (within a walking distance) where they can easily find vacant taxis. In our method, we learn the above knowledge (represented by probabilities) from GPS trajectories of taxis. Then, we feed the knowledge into a probabilistic model which estimates the profit of the candidate locations for a particular driver based on where and when the driver requests for the recommendation. We validate our recommender using historical trajectories generated by over 12,000 taxis during 110 days.

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

 
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Lu, Feng, Xie, Xing and Shaw, Shih-Lung (2011): TDMA'11 workshop overview. In: Proceedings of the 2011 International Conference on Uniquitous Computing 2011. pp. 635-636.

 
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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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Arase, Yuki, Ren, Fei and Xie, Xing (2010): User activity understanding from mobile phone sensors. In: Proceedings of the 2010 International Conference on Uniquitous Computing 2010. pp. 391-392.

Context acquisition is an important technology for ubiquitous computing. An ideal approach would be easy to deploy and non-intrusive to people's life. Mobile phones equipped with advanced sensors are preferable platform owing to their user-friendliness and freedom from extra costs to deploy. In this study, we propose to use a mobile phone to detect user contexts. We formally define the concept of context and then describe applications that leverage people's long-term activity, which can be inferred from their contexts.

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

 
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Zheng, Vincent W., Zheng, Yu, Xie, Xing and Yang, Qiang (2010): Collaborative location and activity recommendations with GPS history data. In: Proceedings of the 2010 International Conference on the World Wide Web 2010. pp. 1029-1038.

With the increasing popularity of location-based services, such as tour guide and location-based social network, we now have accumulated many location data on the Web. In this paper, we show that, by using the location data based on GPS and users' comments at various locations, we can discover interesting locations and possible activities that can be performed there for recommendations. Our research is highlighted in the following location-related queries in our daily life: 1) if we want to do something such as sightseeing or food-hunting in a large city such as Beijing, where should we go? 2) If we have already visited some places such as the Bird's Nest building in Beijing's Olympic park, what else can we do there? By using our system, for the first question, we can recommend her to visit a list of interesting locations such as Tiananmen Square, Bird's Nest, etc. For the second question, if the user visits Bird's Nest, we can recommend her to not only do sightseeing but also to experience its outdoor exercise facilities or try some nice food nearby. To achieve this goal, we first model the users' location and activity histories that we take as input. We then mine knowledge, such as the location features and activity-activity correlations from the geographical databases and the Web, to gather additional inputs. Finally, we apply a collective matrix factorization method to mine interesting locations and activities, and use them to recommend to the users where they can visit if they want to perform some specific activities and what they can do if they visit some specific places. We empirically evaluated our system using a large GPS dataset collected by 162 users over a period of 2.5 years in the real-world. We extensively evaluated our system and showed that our system can outperform several state-of-the-art baselines.

© All rights reserved Zheng 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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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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Arase, Yuki, Xie, Xing, Duan, Manni, Hara, Takahiro and Nishio, Shojiro (2009): A game based approach to assign geographical relevance to web images. In: Proceedings of the 2009 International Conference on the World Wide Web 2009. pp. 811-820.

Geographical context is very important for images. Millions of images on the Web have been already assigned latitude and longitude information. Due to the rapid proliferation of such images with geographical context, it is still difficult to effectively search and browse them, since we do not have ways to decide their relevance. In this paper, we focus on the geographical relevance of images, which is defined as to what extent the main objects in an image match landmarks at the location where the image was taken. Recently, researchers have proposed to use game based approaches to label large scale data such as Web images. However, previous works have not shown the quality of collected game logs in detail and how the logs can improve existing applications. To answer these questions, we design and implement a Web-based and multi-player game to collect human knowledge while people are enjoying the game. Then we thoroughly analyze the game logs obtained during a three week study with 147 participants and propose methods to determine the image geographical relevance. In addition, we conduct an experiment to compare our methods with a commercial search engine. Experimental results show that our methods dramatically improve image search relevance. Furthermore, we show that we can derive geographically relevant objects and their salient portion in images, which is valuable for a number of applications such as image location recognition.

© All rights reserved Arase 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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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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Zheng, Yu, Liu, Like, Wang, Longhao and Xie, Xing (2008): Learning transportation mode from raw gps data for geographic applications on the web. In: Proceedings of the 2008 International Conference on the World Wide Web 2008. pp. 247-256.

Geographic information has spawned many novel Web applications where global positioning system (GPS) plays important roles in bridging the applications and end users. Learning knowledge from users' raw GPS data can provide rich context information for both geographic and mobile applications. However, so far, raw GPS data are still used directly without much understanding. In this paper, an approach based on supervised learning is proposed to automatically infer transportation mode from raw GPS data. The transportation mode, such as walking, driving, etc., implied in a user's GPS data can provide us valuable knowledge to understand the user. It also enables context-aware computing based on user's present transportation mode and design of an innovative user interface for Web users. Our approach consists of three parts: a change point-based segmentation method, an inference model and a post-processing algorithm based on conditional probability. The change point-based segmentation method was compared with two baselines including uniform duration based and uniform length based methods. Meanwhile, four different inference models including Decision Tree, Bayesian Net, Support Vector Machine (SVM) and Conditional Random Field (CRF) are studied in the experiments. We evaluated the approach using the GPS data collected by 45 users over six months period. As a result, beyond other two segmentation methods, the change point based method achieved a higher degree of accuracy in predicting transportation modes and detecting transitions between them. Decision Tree outperformed other inference models over the change point based segmentation method.

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

2006
 
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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.

2005
 
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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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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-Jrg 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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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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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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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.

2003
 
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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.

2002
 
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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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Page maintainer: The Editorial Team
URL: http://www.interaction-design.org/references/authors/xing_xie.html

Publication statistics

Pub. period:2002-2011
Pub. count:31
Number of co-authors:56



Co-authors

Number of publications with 3 favourite co-authors:

Wei-Ying Ma:22
Yu Zheng:8
Hao Liu:4

 

 

Productive colleagues

Xing Xie's 3 most productive colleagues in number of publications:

Wei-Ying Ma:95
Patrick Baudisch:57
Hongjiang Zhang:45
 
 
 

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