An Effective Program Clustering Algorithm for TV Recommendation System

Abstract:

As the increasing popularity of smart TV, providing interactive and personalized TV video recommendation to TV viewers as on internet has been an urgent requirements for TV operators. To recommend a program to viewer satisfying individual interest, the classification of TV programs will be necessary, which should reflect the users' interests. This paper presents an approach for clustering TV programs with using users' VoD(Video on Demand) data. After analyzing the VoD data source, the VoD duration ratio is proposed as the measure of user's interest to a TV program. Through analyzing clustering result, it is observed of the implicit relation of user interest and we explained the relation.

• Periodical: Applied Mechanics and Materials (Volume 721)
• Pages: 757-761
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.721.757
• Online since: December 2014
• Authors: Chao Cheng, Xin Yu Liu, Xing Jun Wang
• Keywords: Program Clustering, Tv Recommendation, User Interest

References

[1] J. Chen, C. Yang, and M. Chen, A study about the personalized recommendation system of tv program based on user behaviors, in Advanced Computational Intelligence (ICACI), 2012 IEEE Fifth International Conference on. IEEE, (2012)498–501.

DOI: 10.1109/icaci.2012.6463214

[2] J. S. Cho Y S, Moon S C, Clustering method using weighted preference based on rfm score for personalized recommendation system in u-commerce, in Ubiquitous Information Technologies and Applications. Springer Berlin Heidelberg, (2014)131–140.

DOI: 10.1007/978-3-642-41671-2_18

[3] Z. S. Wei S, Ye N, Collaborative filtering recommendation algorithm based on item clustering and global similarity, in Business Intelligence and Financial Engineering (BIFE), 2012 Fifth International Conference on. IEEE, (2012) 69–72.

DOI: 10.1109/bife.2012.23

[4] M. B. Shepitsen A, Gemmell J, Personalized recommendation in social tagging systems using hierarchical clustering, in Proceedings of the 2008 ACM conference on Recommender systems. ACM, (2008)259–266.

DOI: 10.1145/1454008.1454048

[5] L. L. Y. De Vleeschauwer D, Hawinkel C, Determining leaders and clusters in video consumption., CNSM, (2013) 382–385.

DOI: 10.1109/cnsm.2013.6727861

[6] P. I. Tsingalis I, Pipilis I, A statistical and clustering study on youtube 2d and 3d video recommendation graph, in Communications, Control and Signal Processing (ISCCSP), 2014 6th International Symposium on. IEEE, (2013) 236–243.

DOI: 10.1109/isccsp.2014.6877872

[7] D. L. I. B. Kirkland O, Experimental evaluation of cluster quality measures, in Computational Intelligence (UKCI), 2013 13th UK Workshop on. IEEE, (2013)294–297.

DOI: 10.1109/ukci.2013.6651311