TY - GEN
T1 - Comparison of techniques for time aware TV channel recommendation
AU - Oh, Sungtak
AU - Kim, Noo Ri
AU - Lee, Jaedong
AU - Lee, Jee Hyong
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/2/18
Y1 - 2014/2/18
N2 - With the increasing number of TV channels, it is more difficult for viewers to find their preferred TV channel. Thus, the recommender system for TV is needed. However, it has several difficulties. First, the viewer's preferred TV channel is different according to the temporal context. Moreover, the sparseness problem also occurs when we consider temporal context. Temporal context has been recognized as an important factor to consider in personalized recommender systems. A lot of time aware recommendation methods were proposed for these difficulties. In this paper, we survey and compare some techniques for time aware TV channel recommendation such as Singular Value Decomposition (SVD), traditional Matrix Factorization (MF), and Temporal Regularized Matrix Factorization (TRMF). We apply them for real-world data to analyze possible benefits of temporal context information for TV channel recommendation and compare the performance of each of them.
AB - With the increasing number of TV channels, it is more difficult for viewers to find their preferred TV channel. Thus, the recommender system for TV is needed. However, it has several difficulties. First, the viewer's preferred TV channel is different according to the temporal context. Moreover, the sparseness problem also occurs when we consider temporal context. Temporal context has been recognized as an important factor to consider in personalized recommender systems. A lot of time aware recommendation methods were proposed for these difficulties. In this paper, we survey and compare some techniques for time aware TV channel recommendation such as Singular Value Decomposition (SVD), traditional Matrix Factorization (MF), and Temporal Regularized Matrix Factorization (TRMF). We apply them for real-world data to analyze possible benefits of temporal context information for TV channel recommendation and compare the performance of each of them.
KW - content-based filtering
KW - matrix factorization
KW - recommender system
KW - temporal context
KW - time aware recommendation
KW - TV program recommendation
UR - https://www.scopus.com/pages/publications/84940464599
U2 - 10.1109/SCIS-ISIS.2014.7044859
DO - 10.1109/SCIS-ISIS.2014.7044859
M3 - Conference contribution
AN - SCOPUS:84940464599
T3 - 2014 Joint 7th International Conference on Soft Computing and Intelligent Systems, SCIS 2014 and 15th International Symposium on Advanced Intelligent Systems, ISIS 2014
SP - 989
EP - 992
BT - 2014 Joint 7th International Conference on Soft Computing and Intelligent Systems, SCIS 2014 and 15th International Symposium on Advanced Intelligent Systems, ISIS 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 Joint 7th International Conference on Soft Computing and Intelligent Systems, SCIS 2014 and 15th International Symposium on Advanced Intelligent Systems, ISIS 2014
Y2 - 3 December 2014 through 6 December 2014
ER -