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Effective Cross Domain Recommendation for TV User

Journal: International Journal of Science and Research (IJSR) (Vol.6, No. 2)

Publication Date:

Authors : ; ; ;

Page : 2101-2104

Keywords : LatentDirichlet Allocation LDA; modelparameter update; social TV; topic model; TV programrecommendation;

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Social TV is a social media service via TV and social network through which TV users exchange their experiences about TV programs that they are viewing. For social TV service, two technical aspects are envisioned grouping of similar TV users to make social TV communities and recommending TV programs based on group and individual interests for personalizing TV. In this, we propose a unified topic model based on grouping of related TV users and recommending TV programs as a social TV service. The proposed unified topic model employs two Latent Dirichlet Allocation (LDA) models. One is a topic model of TV users, and the other is topic model of the description words for viewed TV programs. The two LDA models are then integrated via a topic proportion parameter for TV programs. Our proposed unified topic model will be extended for cross-domain recommendation where three topic models of TV users viewing history data, TV program description data, and web content description data are tied together and an appropriate rank method for cross-domain recommendation is studied. Using the extended unified topic model, effective cross-domain recommendation is expected to be feasible from the TV domain to the web domain for TV users who can then easily select associated web contents for the TV programs that they have enjoyed watching. With the help of unified model we identifies the semantic relation between TV user groups and TV program description word groups so that more meaningful TV program recommendations can be made. The unified topic model also overwhelms an item ramp-up problem such that new TV programs can be reliably recommended to TV users. Furthermore, from the topic model of TV users, TV users with related tastes can be grouped as topics, which can then be recommended as social TV communities.

Last modified: 2021-06-30 17:48:27