Proceedings of the seventh international conference (1990) on Machine learning
Automatic personalization based on Web usage mining
Communications of the ACM
Interactive query expansion: a user-based evaluation in a relevance feedback environment
Journal of the American Society for Information Science
Discovery and Evaluation of Aggregate Usage Profiles for Web Personalization
Data Mining and Knowledge Discovery
Machine Learning for User Modeling
User Modeling and User-Adapted Interaction
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Adaptive web search based on user profile constructed without any effort from users
Proceedings of the 13th international conference on World Wide Web
Display time as implicit feedback: understanding task effects
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Improving recommendation lists through topic diversification
WWW '05 Proceedings of the 14th international conference on World Wide Web
Active feedback in ad hoc information retrieval
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
The loquacious user: a document-independent source of terms for query expansion
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Automatic identification of user interest for personalized search
Proceedings of the 15th international conference on World Wide Web
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
Improving web search ranking by incorporating user behavior information
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
User modeling meets usability goals
UM'05 Proceedings of the 10th international conference on User Modeling
Task-Oriented web user modeling for recommendation
UM'05 Proceedings of the 10th international conference on User Modeling
Capturing the User's Reading Context for Tailoring Summaries
UMAP '09 Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization: formerly UM and AH
A split-list approach for relevance feedback in information retrieval
Information Processing and Management: an International Journal
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Personalization is one of the important research issues in the areas of information retrieval and Web search. Providing personalized services that are tailored toward the specific preferences and interests of a given user can enhance her experience and satisfaction. However, to effectively capture user interests is a challenging research problem. Some challenges include how to quickly capture user interests in an unobtrusive way, how to provide diversified recommendations, and how to track the drifts of user interests in a timely fashion. In this paper, we propose a model for learning user interests and an algorithm that actively captures user interests through an interactive recommendation process. The key advantage of our algorithm is that it takes into account both exploitation(recommending items that belong to users' core interest) and exploration(discovering potential interests of users). Extensive experiments using synthetic data and a user study show that our algorithm can quickly capture diversified user interests in an unobtrusive way, even when the user interests may drift along time.