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.
%0 Journal Article
%1 CLC09
%A Chang, Jui-Hung
%A Lai, Chin-Feng
%A Huang, Yueh-Min
%A Chao, Han-Chieh
%C VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
%D 2010
%I SPRINGER
%J MULTIMEDIA TOOLS AND APPLICATIONS
%K epg hpi_ism10 recommender tv
%N 1
%P 31-48
%R 10.1007/s11042-009-0405-6
%T 3PRS: a personalized popular program recommendation system for digital
TV for P2P social networks
%V 47
%X 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.
@article{CLC09,
abstract = {{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.}},
added-at = {2010-03-25T16:07:08.000+0100},
address = {{VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS}},
affiliation = {{Chang, JH (Reprint Author), Natl Cheng Kung Univ, Dept Engn Sci, Tainan 701, Taiwan.
{[}Chang, Jui-Hung; Lai, Chin-Feng; Huang, Yueh-Min] Natl Cheng Kung Univ, Dept Engn Sci, Tainan 701, Taiwan.
{[}Chao, Han-Chieh] Natl Ilan Univ, Inst Comp Sci \& Informat Engn, Ilan 260, Taiwan.
{[}Chao, Han-Chieh] Natl Ilan Univ, Dept Elect Engn, Ilan 260, Taiwan.
{[}Chao, Han-Chieh] Natl Dong Hwa Univ, Dept Elect Engn, Hualien 974, Taiwan.}},
author = {Chang, Jui-Hung and Lai, Chin-Feng and Huang, Yueh-Min and Chao, Han-Chieh},
author-email = {{changrh@mail.ncku.edu.tw
cinfon@www.mmn.es.ncku.edu.tw
huang@mail.ncku.edu.tw
hcc@mail.niu.edu.tw}},
biburl = {https://www.bibsonomy.org/bibtex/25f6cbdd676f668b0f1cb5bf6c6455473/datentaste},
doc-delivery-number = {{554LV}},
doi = {{10.1007/s11042-009-0405-6}},
interhash = {59f93fb679c3e808a1011024d30db493},
intrahash = {5f6cbdd676f668b0f1cb5bf6c6455473},
issn = {{1380-7501}},
journal = {{MULTIMEDIA TOOLS AND APPLICATIONS}},
keywords = {epg hpi_ism10 recommender tv},
month = {{MAR}},
number = {{1}},
number-of-cited-references = {{27}},
pages = {{31-48}},
publisher = {{SPRINGER}},
timestamp = {2010-03-25T16:07:08.000+0100},
title = {{3PRS: a personalized popular program recommendation system for digital
TV for P2P social networks}},
type = {{Article}},
unique-id = {{ISI:000274437400003}},
volume = {{47}},
year = {{2010}}
}