Digital TV Program Recommendation System Based on Collaboration Filtering

2014 ◽  
pp. 531-538
Author(s):  
Fulian Yin ◽  
Jianping Chai ◽  
Hanlei Wang ◽  
Ya Gao
Keyword(s):  
Recommendation System ◽  
Digital Tv ◽  
Tv Program

Study on Personalized Recommendation Technology of Digital TV Programs

2013 ◽  
Vol 347-350 ◽  
pp. 3035-3038
Author(s):  
Xiao Bin Wang ◽  
Qing Jun Wang
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Digital Tv ◽  
Personalized Recommendation ◽  
Digital Television ◽  
Content Filtering ◽  
Recommendation Algorithm ◽  
Tv Program ◽  
Collaborative Filtering Algorithm ◽  
Multi Agent

This paper aims at one of key technologies in digital television development ---intelligent personalized recommendation technology of digital TV programs for study. This paper proposes to take advantage of ample TV-Anytime to describe metadata so as to perform specific plans of guide service for TV programs based on TV-Anytime metadata specification. It combines technology such as data mining and artificial intelligence etc with a view of building a personalized TV program recommendation system on the framework of the multi-agent. Besides, a hybrid algorithm with content filtering and collaborative filtering based on the systematical recommendation algorithm has been put forward. In order to overcome the deficiencies of traditional collaborative filtering algorithm which relies on users explicit evaluation, the paper represents an improved algorithm with the footing of content collaborative filtering.

A fair scheduler using cloud computing for digital TV program recommendation system

2014 ◽  
Vol 60 (1) ◽  
pp. 55-66 ◽  
Author(s):  
Jui-Hung Chang ◽  
Chin-Feng Lai ◽  
Ming-Shi Wang
Keyword(s):  
Cloud Computing ◽  
Recommendation System ◽  
Digital Tv ◽  
Tv Program ◽  
Fair Scheduler

Digital TV Program Recommendation System Based on Latent Factor Model

2014 ◽  
Vol 513-517 ◽  
pp. 1692-1695
Author(s):  
Fu Lian Yin ◽  
Jian Ping Chai ◽  
Nan Li ◽  
Ya Gao
Keyword(s):  
Recommendation System ◽  
System Analysis ◽  
Factor Model ◽  
Digital Tv ◽  
System Structure ◽  
Analysis Method ◽  
Latent Factor ◽  
Program Type ◽  
Tv Program ◽  
Latent Factor Model

In order to solve the problem of information overload, which is a consequence of the extreme abundance of digital television (TV) program resource, this paper proposed a digital TV program recommendation system based on latent factor model (LFM). This system constructed a digital TV program recommendation system structure including information inputting unit, system analysis unit and information recommendation sending unit. The proposed program type analysis method searches for the relation between audience interest and the programs which audiences watch. To classify the audience crowd, there are two types of analysis method which are program type threshold analysis and program type cluster analysis that based on artificial classification of experience value and automatic clustering with arbitrary number of clusters respectively. The feasibility of the algorithm has been proved by simulated analysis.

A cloud-based intelligent TV program recommendation system

2013 ◽  
Vol 39 (7) ◽  
pp. 2379-2399 ◽  
Author(s):  
Jui-Hung Chang ◽  
Chin-Feng Lai ◽  
Ming-Shi Wang ◽  
Tin-Yu Wu
Keyword(s):  
Recommendation System ◽  
Tv Program

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

2009 ◽  
Vol 47 (1) ◽  
pp. 31-48 ◽  
Author(s):  
Jui-Hung Chang ◽  
Chin-Feng Lai ◽  
Yueh-Min Huang ◽  
Han-Chieh Chao
Keyword(s):  
Social Networks ◽  
Recommendation System ◽  
Digital Tv
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