Number of co-authors:9
Number of publications with 3 favourite co-authors:Kam-Fai Wong:5Ming Zhou:3Cheng Niu:3
Wei Gao's 3 most productive colleagues in number of publications:Jian-Yun Nie:33Caroline Haythornt..:19Kam-Fai Wong:17
... in an information-rich world, the wealth of information means a dearth of something else: a scarcity of whatever it is that information consumes. What information consumes is rather obvious: it consumes the attention of its recipients. Hence a wealth of information creates a poverty of attention, and a need to allocate that attention efficiently among the overabundance of information sources that might consume it.
-- Herbert Simon in "Computers, Communications and the Public Interest," 1971
Marc Hassenzahl explains the fascinating concept of User Experience and Experience Design. Commentaries by Don Norman, Eric Reiss, Mark Blythe, and Whitney Hess
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Publications by Wei Gao (bibliography)
Cai, Peng, Gao, Wei, Zhou, Aoying and Wong, Kam-Fai (2011): Relevant knowledge helps in choosing right teacher: active query selection for ranking adaptation. In: Proceedings of the 34th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2011. pp. 115-124.
Learning to adapt in a new setting is a common challenge to our knowledge and capability. New life would be easier if we actively pursued supervision from the right mentor chosen with our relevant but limited prior knowledge. This variant principle of active learning seems intuitively useful to many domain adaptation problems. In this paper, we substantiate its power for advancing automatic ranking adaptation, which is important in web search since it's prohibitive to gather enough labeled data for every search domain for fully training domain-specific rankers. For the cost-effectiveness, it is expected that only those most informative instances in target domain are collected to annotate while we can still utilize the abundant ranking knowledge in source domain. We propose a unified ranking framework to mutually reinforce the active selection of informative target-domain queries and the appropriate weighting of source training data as related prior knowledge. We select to annotate those target queries whose documents' order most disagrees among the members of a committee built on the mixture of source training data and the already selected target data. Then the replenished labeled set is used to adjust the importance of source queries for enhancing their rank transfer. This procedure iterates until labeling budget exhausts. Based on LETOR3.0 and Yahoo! Learning to Rank Challenge data sets, our approach significantly outperforms the random query annotation commonly used in ranking adaptation and the active rank learner on target-domain data only.
© All rights reserved Cai et al. and/or ACM Press
Gao, Wei and Haythornthwaite, Caroline (2011): Learning and knowledge exchange in science teaching. In: Proceedings of the 2011 iConference 2011. pp. 676-678.
This poster presents our study of how social networks support learning and knowledge exchange among participants of a professional development program for science teachers. Social networks data and qualitative interviews were used to assess participants' current learning networks as well as their perceptions, practices, and experiences with their current learning environment and innovation pertaining to science teaching and learning. Results indicate that while the majority of the connections were confined to their school buildings, teachers acquire a considerable amount of new knowledge from networks outside these buildings. The interviews revealed a common desire among participants to foster new connections and talk to others about science teaching and learning, and show the important role of information from outside the school as contributing to new activities. The interviews also provide insight into the kinds of interactions that bond the teachers with their colleagues and the kinds of learning that are given or received in science teachers' everyday practice.
© All rights reserved Gao and Haythornthwaite and/or ACM Press
Gao, Wei, Cai, Peng, Wong, Kam-Fai and Zhou, Aoying (2010): Learning to rank only using training data from related domain. In: Proceedings of the 33rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2010. pp. 162-169.
Like traditional supervised and semi-supervised algorithms, learning to rank for information retrieval requires document annotations provided by domain experts. It is costly to annotate training data for different search domains and tasks. We propose to exploit training data annotated for a related domain to learn to rank retrieved documents in the target domain, in which no labeled data is available. We present a simple yet effective approach based on instance-weighting scheme. Our method first estimates the importance of each related-domain document relative to the target domain. Then heuristics are studied to transform the importance of individual documents to the pairwise weights of document pairs, which can be directly incorporated into the popular ranking algorithms. Due to importance weighting, ranking model trained on related domain is highly adaptable to the data of target domain. Ranking adaptation experiments on LETOR3.0 dataset  demonstrate that with a fair amount of related-domain training data, our method significantly outperforms the baseline without weighting, and most of time is not significantly worse than an "ideal" model directly trained on target domain.
© All rights reserved Gao et al. and/or their publisher
Gao, Wei, Niu, Cheng, Nie, Jian-Yun, Zhou, Ming, Wong, Kam-Fai and Hon, Hsiao-Wuen (2010): Exploiting query logs for cross-lingual query suggestions. In ACM Transactions on Information Systems, 28 (2) p. 6.
Query suggestion aims to suggest relevant queries for a given query, which helps users better specify their information needs. Previous work on query suggestion has been limited to the same language. In this article, we extend it to cross-lingual query suggestion (CLQS): for a query in one language, we suggest similar or relevant queries in other languages. This is very important to the scenarios of cross-language information retrieval (CLIR) and other related cross-lingual applications. Instead of relying on existing query translation technologies for CLQS, we present an effective means to map the input query of one language to queries of the other language in the query log. Important monolingual and cross-lingual information such as word translation relations and word co-occurrence statistics, and so on, are used to estimate the cross-lingual query similarity with a discriminative model. Benchmarks show that the resulting CLQS system significantly outperforms a baseline system that uses dictionary-based query translation. Besides, we evaluate CLQS with French-English and Chinese-English CLIR tasks on TREC-6 and NTCIR-4 collections, respectively. The CLIR experiments using typical retrieval models demonstrate that the CLQS-based approach has significantly higher effectiveness than several traditional query translation methods. We find that when combined with pseudo-relevance feedback, the effectiveness of CLIR using CLQS is enhanced for different pairs of languages.
© All rights reserved Gao et al. and/or ACM Press
Gao, Wei, Niu, Cheng, Zhou, Ming and Wong, Kam-Fai (2009): Joint Ranking for Multilingual Web Search. In: Boughanem, Mohand, Berrut, Catherine, Mothe, Josiane and Soulé-Dupuy, Chantal (eds.) Advances in Information Retrieval - 31th European Conference on IR Research - ECIR 2009 April 6-9, 2009, 2009, Toulouse, France. pp. 114-125.
Gao, Wei, Niu, Cheng, Nie, Jian-Yun, Zhou, Ming, Hu, Jian, Wong, Kam-Fai and Hon, Hsiao-Wuen (2007): Cross-lingual query suggestion using query logs of different languages. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2007. pp. 463-470.
Query suggestion aims to suggest relevant queries for a given query, which help users better specify their information needs. Previously, the suggested terms are mostly in the same language of the input query. In this paper, we extend it to cross-lingual query suggestion (CLQS): for a query in one language, we suggest similar or relevant queries in other languages. This is very important to scenarios of cross-language information retrieval (CLIR) and cross-lingual keyword bidding for search engine advertisement. Instead of relying on existing query translation technologies for CLQS, we present an effective means to map the input query of one language to queries of the other language in the query log. Important monolingual and cross-lingual information such as word translation relations and word co-occurrence statistics, etc. are used to estimate the cross-lingual query similarity with a discriminative model. Benchmarks show that the resulting CLQS system significantly out performs a baseline system based on dictionary-based query translation. Besides, the resulting CLQS is tested with French to English CLIR tasks on TREC collections. The results demonstrate higher effectiveness than the traditional query translation methods.
© All rights reserved Gao et al. and/or ACM Press
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