Utilizing Users' Watching Sequences and TV-programs' Metadata for Personalized TV-program Recommendation

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Recently, the explosive growth of digital video contents including IPTV (Internet Protocol Television) has led to the need of recommendation system to guide users among various and huge amount of entertainment movies, live-TV or related services that are called TV programs in general. Consequently, recommendation system has become a general tool to support user's decision in making choice. Most of the ever-proposed algorithms focus on the prediction accuracy; however, we also have to support the diversity of the recommendation results to surprise users in order to widen their choices that might be just missed if the accuracy is only focused on. In this paper, we introduce a new model-based top-K recommendation algorithm called "watch-flow algorithm" for selecting the next K highest potential TV programs that user might like. Our model utilizes users' watching sequences and TV program metadata to identify the recommending value for each TV program. Furthermore, this model is also capable of giving a personalized recommendation for a specific user based on his/her watching sequence, as well as capable to improve the prediction accuracy and the diversity. We apply our algorithm on a random sample of users' watching sequences in a dataset collected from real users' log. According to the experimental results, our proposed method shows better performance in recommendation than that of ever-proposed algorithms in terms of higher accuracy while keeping the coverage of programs in high rate.
一般社団法人電子情報通信学会の論文
2012-07-12

Utilizing Users' Watching Sequences and TV-programs' Metadata for Personalized TV-program Recommendation

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