Adaptive Temporal Model for IPTV Recommendation
How to help the IPTV service provider make the program recommendation to their clients is the problem we propose to solve in this paper. Here we offer an adaptive temporal model to identify multiple members under a shared IPTV account. The time intervals are first detected and defined in each account. Then, the preference similarity is calculated among the intervals to extract the members. After that, we evaluate our model on the industrial data sets by a famous IPTV provider. The experimental results show that our proposed model is promising and outperform the state-of-the-art algorithms with low computational complexity and versatility without user feedback. Furthermore, the proposed model has been officially adopted by the IPTV provider and applied in their IPTV systems with excellent user satisfaction in 2013.
KeywordsRecommender System Subspace Cluster Personalized Recommendation IPTV Service Program Recommendation
Unable to display preview. Download preview PDF.
- 3.Adomavicius, G., Tuzhilin, A.: Context-aware recommender systems. In: Recommender Systems Handbook, pp. 217–253. Springer (2011)Google Scholar
- 4.Candillier, L., Chevalier, M., Dudognon, D., Mothe, J.: Multiple similarities for diversity in recommender systems. International Journal On Advances in Intelligent Systems 5(3 and 4), 234–246 (2012)Google Scholar
- 5.Chen, Q., Yang, Y., Hu, Q., He, L.: Locating query-oriented experts in microblog search. In: Proceedings of Workshop on Semantic Matching in Information Retrieval Co-located with the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval, SMIR@SIGIR 2014, Queensland, Australia, July 11, 2014, pp. 16–23 (2014)Google Scholar
- 8.Said, A., De Luca, E.W., Albayrak, S.: Inferring contextual user profiles-improving recommender performance. In: Proceedings of the 3rd RecSys Workshop on Context-Aware Recommender Systems (2011)Google Scholar
- 9.Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web, WWW 2001, pp. 285–295 (2001)Google Scholar
- 11.Zhang, A., Fawaz, N., Ioannidis, S., Montanari, A.: Guess who rated this movie: Identifying users through subspace clustering (2012). arXiv preprint arXiv:1208.1544