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Time-dependent user profiling for TV recommendation

Authors
Oh J.Sung Y.Kim J.Humayoun M.Park Y.-H.Yu H.
Issue Date
Nov-2012
Publisher
IEEE
Keywords
EMD; Novel recommendation; Personal Popularity Tendency
Citation
2012 Second International Conference on Cloud and Green Computing, v.2013-FEB, pp 783 - 787
Pages
5
Journal Title
2012 Second International Conference on Cloud and Green Computing
Volume
2013-FEB
Start Page
783
End Page
787
URI
https://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/12354
DOI
10.1109/CGC.2012.119
ISSN
0000-0000
Abstract
TV is one of the most important sources of media content consumption. The large amount of TV channels and programs have overwhelm audiences. It poses difficulties for viewers in finding their preferred programs. Tools for searching TV programs such as TV guides and PreVue channel are designed for general public and do not provide personalized recommendation. Developing an effective recommender system for TV is challenging because a TV is often shared by multiple people (e.g., family members) without login, and thus it is hard to acquire individual TV watch log, which is crucial to build an effective recommendation. Existing recommender systems for social networks or web commerce are devised for handling one user per account, and thus are not proper for TV recommender system. This paper proposes a time dependent user profiling technique. Particularly, we do time based analysis in which we first split watch log into certain time slots, and re-merge consecutive time slots by using a clustering technique. Evaluation results show that the proposed method produces higher accuracy than a typical profiling technique. © 2012 IEEE.
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