Presentation 2012/7/12
Utilizing Users' Watching Sequences and TV-programs' Metadata for Personalized TV-program Recommendation
DINH QUOC HUNG, SRIPRASERTSUK PAO, WATARU KAMEYAMA, KENJI FUKUDA,
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Abstract(in English) 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.
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Paper # Vol.2012-AVM-77No.11
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Committee MoMuC
Conference Date 2012/7/12(1days)
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Registration To Mobile Multimedia Communications(MoMuC)
Language ENG
Title (in Japanese) (See Japanese page)
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Title (in English) Utilizing Users' Watching Sequences and TV-programs' Metadata for Personalized TV-program Recommendation
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1st Author's Name DINH QUOC HUNG
1st Author's Affiliation GITS Waseda University()
2nd Author's Name SRIPRASERTSUK PAO
2nd Author's Affiliation GITS Waseda University
3rd Author's Name WATARU KAMEYAMA
3rd Author's Affiliation GITS Waseda University
4th Author's Name KENJI FUKUDA
4th Author's Affiliation WOWOW Inc.
Date 2012/7/12
Paper # Vol.2012-AVM-77No.11
Volume (vol) vol.112
Number (no) 135
Page pp.pp.-
#Pages 6
Date of Issue