Users' perception of the performance of a filtering system
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
Real life, real users, and real needs: a study and analysis of user queries on the web
Information Processing and Management: an International Journal
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Modern Information Retrieval
Personalized web search by mapping user queries to categories
Proceedings of the eleventh international conference on Information and knowledge management
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Understanding user goals in web search
Proceedings of the 13th international conference on World Wide Web
Accurately interpreting clickthrough data as implicit feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Learning user interaction models for predicting web search result preferences
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Investigating behavioral variability in web search
Proceedings of the 16th international conference on World Wide Web
To personalize or not to personalize: modeling queries with variation in user intent
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
A user browsing model to predict search engine click data from past observations.
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
A dynamic bayesian network click model for web search ranking
Proceedings of the 18th international conference on World wide web
Click-through prediction for news queries
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Ranking specialization for web search: a divide-and-conquer approach by using topical RankSVM
Proceedings of the 19th international conference on World wide web
Mining Query Logs: Turning Search Usage Data into Knowledge
Foundations and Trends in Information Retrieval
Adapting boosting for information retrieval measures
Information Retrieval
Understanding temporal query dynamics
Proceedings of the fourth ACM international conference on Web search and data mining
User-click modeling for understanding and predicting search-behavior
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 20th ACM international conference on Information and knowledge management
Probabilistic models for personalizing web search
Proceedings of the fifth ACM international conference on Web search and data mining
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Searchers' information needs are diverse and cover a broad range of topics; hence, it is important for search engines to accurately understand each individual user's search intents in order to provide optimal search results. Search log data, which records users' search behaviors when interacting with search engines, provides a valuable source of information about users' search intents. Therefore, properly characterizing the heterogeneity among the users' observed search behaviors is the key to accurately understanding their search intents and to further predicting their behaviors. In this work, we study the problem of user modeling in the search log data and propose a generative model, dpRank, within a non-parametric Bayesian framework. By postulating generative assumptions about a user's search behaviors, dpRank identifies each individual user's latent search interests and his/her distinct result preferences in a joint manner. Experimental results on a large-scale news search log data set validate the effectiveness of the proposed approach, which not only provides in-depth understanding of a user's search intents but also benefits a variety of personalized applications.