Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Siteseer: personalized navigation for the Web
Communications of the ACM
Clustering Approach for Hybrid Recommender System
WI '03 Proceedings of the 2003 IEEE/WIC International Conference on Web Intelligence
A Hybrid Movie Recommender System Based on Neural Networks
ISDA '05 Proceedings of the 5th International Conference on Intelligent Systems Design and Applications
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In this paper, a new program recommendation algorithm is proposed to recommend user preferred VOD program in IPTV environment. A proposed system is implemented with hybrid filtering method that can cooperatively complements the shortcomings of the content-based filtering and collaborative filtering. For a user program preference, a single-scaled measure is designed so that the recommendation performance between content-based filtering and collaborative filtering is easily compared and reflected to final hybrid filtering procedure. In order to provide a high quality of program recommendation, we use not only the user watching history, but also the user program preference and mid-subgenre program preference updated weekly as a user preference profile. System performance is evaluated with modified IPTV data from real 24-weeks cable TV watching data provided by Nilson Reseach Corp. in Korea and it shows quite comparative quality of recommendation.