Learning implicit user interest hierarchy for context in personalization
Proceedings of the 8th international conference on Intelligent user interfaces
The Journal of Machine Learning Research
Personalized Web Search For Improving Retrieval Effectiveness
IEEE Transactions on Knowledge and Data Engineering
Ontology-based personalized search and browsing
Web Intelligence and Agent Systems
Interest-based personalized search
ACM Transactions on Information Systems (TOIS)
Utilizing Search Intent in Topic Ontology-Based User Profile for Web Mining
WI '06 Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence
Video search reranking through random walk over document-level context graph
Proceedings of the 15th international conference on Multimedia
Exploring folksonomy for personalized search
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Personalized recommendation in social tagging systems using hierarchical clustering
Proceedings of the 2008 ACM conference on Recommender systems
Proceedings of the 18th international conference on World wide web
Deriving Concept-Based User Profiles from Search Engine Logs
IEEE Transactions on Knowledge and Data Engineering
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Online videos are becoming popular these days. Personalized search has been recognized as effective solution for user accessing desired information when facing a daunting volume of videos. Personalized query understanding serves as one of the most challenges in personalized search, which indicates that unique query has distributed meanings and produce different semantics for different users. Take query of celebrity as example, many celebrities are engaged in multiple fields and certain user may be just interested in the field of videos related to his/her own preference. In this paper, we address the challenge of personalized query understanding by focusing on the problem of personalized celebrity video search. An interest-popularity cross-space mining based method is proposed for solution. Specifically, celebrity popularity and user interest distributions are first learned by topic modeling from heterogeneous data of expert knowledge and user online activities, respectively. We then exploit topic-word distribution refinement to correlate the two heterogeneous topic spaces. Finally the candidate videos are re-ranked based on the derived interest-popularity correlations. Carefully designed experiments have demonstrated the effectiveness of the proposed method. The obtained ranking list is highly consistent with the test users' preferences.