Personalized Celebrity Video Search Based on Cross-Space Mining

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.