Abstract
In order to solve the problem of information overload brought by over abundant digital television (TV) program resources, this paper proposed a digital TV program recommendation system based on collaborative filtering (CF) which contains information inputting unit, system analysis unit and recommendation sending unit. The audience behavior analysis proposed in this paper was based on two forms: personalized audience behaviour analysis and group audience behaviour analysis, which can recommend interesting TV programs suited for the particular individual or group. The result of simulation proves the feasibility of algorithms.
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References
Chunshan, M., Huaying, S.: User activity-based CF Algorithm in Value-Added Services. In: 2011 International Conference on Management Science and Industrial Engineering, MSIE, pp. 793–798 (2011)
Eunhui, K., Shinjee, P., Park, E.: An automatic Recommendation Scheme of TV program Contents for (IP)TV Personalization. IEEE Transaction on Broadcasting 57(3), 674–684 (2011)
Hongguang, Z., Shibao, Z.: Personalized TV Program Recommendation Based on TV-Anytime Metadata. In: Proceedings of the Ninth International Symposium on Consumer Electronics, ISCE 2005, pp. 242–246 (2005)
Linting, G., Hailing, L.: Recommend Items for User in Social Networking Services with CF. In: 2012 International Conference on Computer Science & Service System, CSSS 2012, pp. 1347–1350 (2012)
Tiraweerakhajohn, C., Pinngern, O.: Finding Item Neighbours in Item-Based Collaborative Filtering by Adding Item Content. In: 8th lntematlonal Conference on Control, Automation, Robotics and Vision, ICARCV, vol. 3, pp. 1674–1678 (2004)
XiaoYan, S., HongWu, Y., SongJie, G.: A Personalized Recommender Integrating Item-Based and User-Based Collaborative Filtering. In: 2008 International Seminar on Business and Information Management, ISBIM 2008, pp. 264–267 (2008)
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Yin, F., Chai, J., Wang, H., Gao, Y. (2014). Digital TV Program Recommendation System Based on Collaboration Filtering. In: Pan, L., Păun, G., Pérez-Jiménez, M.J., Song, T. (eds) Bio-Inspired Computing - Theories and Applications. Communications in Computer and Information Science, vol 472. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45049-9_86
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DOI: https://doi.org/10.1007/978-3-662-45049-9_86
Publisher Name: Springer, Berlin, Heidelberg
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