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

被引：17
|
作者：
Chang, Jui-Hung [1 ]
Lai, Chin-Feng [1 ]
Huang, Yueh-Min [1 ]
Chao, Han-Chieh [2 ,3 ,4 ]
机构：
[1] Natl Cheng Kung Univ, Dept Engn Sci, Tainan 701, Taiwan
[2] Natl Ilan Univ, Inst Comp Sci & Informat Engn, Ilan 260, Taiwan
[3] Natl Ilan Univ, Dept Elect Engn, Ilan 260, Taiwan
[4] Natl Dong Hwa Univ, Dept Elect Engn, Hualien 974, Taiwan
来源：
MULTIMEDIA TOOLS AND APPLICATIONS | 2010年 / 47卷 / 01期
关键词：
Digital video broadcasting-terrestrial (DVB-T); Electronic program guide (EPG); Information retrieval (IR); K-nearest neighbo (kNN); k-means;
D O I：
10.1007/s11042-009-0405-6
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
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 (DVBT) 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.
引用
收藏
页码：31 / 48
页数：18
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