Abstract
Abstract As a number of TV programs broadcast today, researches about TV program recommender system have been studied and many researchers have been studying recommende r system to produce recommendation with high accuracy. Recommender system recommends TV program to user by using metadata like genre, plot or calculating users’ preferences about TV programs. In th is paper, we propose a new TV program Collaborative Filtering Recommender System that exploits viewin g time pattern like viewing ratio, relation with finish time and recently viewing history to calculate prefe rence for high-quality of recommendation. To verify usefulness of our research, we also compare our method wh ich utilizes viewing time patterns and baseline which simply recommends TV program of user's most freq uently watched channel. Through this ex-periments, we show that our method very effectively works and r ecommendation performance increases.Key Words : TV Program, Recommender System, Viewing time Patterns, Collaborative Filtering Received: Mar. 22, 2015Revised : Apr. 5, 2015Accepted: Sep. 15, 2015
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Journal of Korean Institute of Intelligent Systems
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
