(An) LDA-based unified topic model for cross domain TV program-web content recommendation

Abstract

With advent of smart TV and social TV services, TV users can consume not only TV programs but also various web contents. Therefore, for smart TV services, it is natural to recommend the related web contents to TV viewers while they are viewing TV programs. In order to do so, it is required that (i) personalized recommendation be possible

(ii) grouping of similar taste users be necessary

and (ii) recommendation of web contents that are highly semantically related with TV program contents be possible. In this dissertation, we propose a unified topic model for similar taste user grouping and cross-domain recommendation between TV and web domains based probabilistic association between TV programs and web contents. Our proposed unified topic model is based on three Latent Dirichlet Allocation (LDA) models: the first one is a topic model of TV users, the second one is a topic model of the description words for watched TV programs, and the last one is a topic model of description words for web video contents. Our unified topic model identifies the semantic relation between TV user groups and description word groups so that more meaningful TV program recommendations and cross-domain recommendation can be made. The unified topic model also overcomes an item ramp-up problem such that new TV programs and new web contents can reliably be recommended for TV users. In order to verify our proposed method of unified topic modeling based TV user grouping, TV program recommendation and cross-domain web content recommendation for social TV services, we use real TV watching history data, EPG data for seven months collected by a TV poll agency and web content description data from YouTube. The experimental results show that the proposed unified topic model yields an average 81.4% precision for 50 topics in TV program recommendation.