Paper Info
Title
Time-Dependent User Profiling for TV Recommendation
Abstract
TV is one of the most important sources of media content consumption. The large amount of TV channels and programs have overwhelm audiences. It poses difficulties for viewers in finding their preferred programs. Tools for searching TV programs such as TV guides and PreVue channel are designed for general public and do not provide personalized recommendation. Developing an effective recommender system for TV is challenging because a TV is often shared by multiple people (e.g., family members) without login, and thus it is hard to acquire individual TV watch log, which is crucial to build an effective recommendation. Existing recommender systems for social networks or web commerce are devised for handling one user per account, and thus are not proper for TV recommender system. This paper proposes a time dependent user profiling technique. Particularly, we do time based analysis in which we first split watch log into certain time slots, and re-merge consecutive time slots by using a clustering technique. Evaluation results show that the proposed method produces higher accuracy than a typical profiling technique.
Year
DOI
Venue
2012
10.1109/CGC.2012.119
CGC
Keywords
Field
DocType
web commerce,time-dependent user profiling,time based analysis,tv recommendation,certain time slot,pattern clustering,media content consumption,prevue channel,tv guide,tv recommender system,tv watch log,time dependent user,social networks,time dependent user profiling technique,consecutive time slots,recommender systems,novel recommendation,tv programs,tv channels,re-merge consecutive time slot,emd,effective recommender system,individual tv watch log,internet,personal popularity tendency,tv guides,social networking (online),user modelling,tv program,clustering technique,personalized recommendation,digital television,tv channel
Recommender system,World Wide Web,Profiling (computer programming),Computer science,Login,Interactive television,Digital television,Cluster analysis,Multimedia,Web commerce,The Internet
Conference
ISBN
Citations 
PageRank 
978-1-4673-3027-5
3
0.40
References 
Authors
17
6
Name
Order
Citations
PageRank
Jinoh Oh130315.32
Youngchul Sung262145.85
Jin-ha Kim332918.78
Muhammad Humayoun492.88
Young-Ho Park5232.52
Hwanjo Yu61715114.02