3PRS: A personalized popular program recommendation system for digital TV for P2P social networks

研究成果: Article

19 引文 斯高帕斯(Scopus)

摘要

Digital TV channels require users to spend more time to choose their favorite TV programs. Electronic Program Guides (EPG) cannot be used to find popular TV programs. Hence, this paper proposes a personalized Digital Video Broadcasting - Terrestrial(DVB-T) Digital TV program recommendation system for P2P social networks. From the DVB-T signal, we obtain EPG of TV programs. The frequency and duration of the programs that users have watched are used to extract programs that users are interested in. The information is collected and weighted by Information Retrieval (IR). The program information is then clustered by k-means. Clusters of users are also grouped by k-means to find cluster relationships. In each group, we decide the most popular program in the group according to the program weight of the channel. When a new user begins to watch the TV program, the K-Nearest Neighbor (kNN) classification method is used to determine the user's predicted cluster label. Then, our system recommends popular programs in the predicted cluster and similar clusters.

原文English
頁(從 - 到)31-48
頁數18
期刊Multimedia Tools and Applications
47
發行號1
DOIs
出版狀態Published - 2010 三月 1

All Science Journal Classification (ASJC) codes

  • Software
  • Media Technology
  • Hardware and Architecture
  • Computer Networks and Communications

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