- Automatic Recommendation of (IP)TV programs based on A Rank Model using Collaborative Filtering
- Kim, Eun-Hui ; Pyo, Shin-Jee ; Kim, Mun-Churl ;
- Journal of Broadcast Engineering, volume 14, issue 2, 2009, Pages 238~252
- DOI : 10.5909/JBE.2009.14.2.238
Abstract
Due to the rapid increase of available contents via the convergence of broadcasting and internet, the efficient access to personally preferred contents has become an important issue. In this paper, for recommendation scheme for TV programs using a collaborative filtering technique is studied. For recommendation of user preferred TV programs, our proposed recommendation scheme consists of offline and online computation. About offline computation, we propose reasoning implicitly each user`s preference in TV programs in terms of program contents, genres and channels, and propose clustering users based on each user`s preferences in terms of genres and channels by dynamic fuzzy clustering method. After an active user logs in, to recommend TV programs to the user with high accuracy, the online computation includes pulling similar users to an active user by similarity measure based on the standard preference list of active user and filtering-out of the watched TV programs of the similar users, which do not exist in EPG and ranking of the remaining TV programs by proposed rank model. Especially, in this paper, the BM (Best Match) algorithm is extended to make the recommended TV programs be ranked by taking into account user`s preferences. The experimental results show that the proposed scheme with the extended BM model yields 62.1% of prediction accuracy in top five recommendations for the TV watching history of 2,441 people.