An IPTV Recommendation Method Based on Latent Context

Abstract:

In the light of the data differences between network television and the Internet, this paper solve the problem of grading IPTV by the introduction of time context information and computing the latent scores based on the traditional and item-based collaborative filtering recommendation algorithm. Construct the user - item, the item - time model and optimize item similarity calculation so as to ease the difficulty of searching the similar item due to the data scarcity. The experimental results show that the improved method can obviously increase the recommendation precision and has a certain effect on reducing the impact of data scarcity compared with the traditional item-based collaborative filtering.

• Periodical: Applied Mechanics and Materials (Volumes 668-669)
• Pages: 1237-1242
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.668-669.1237
• Online since: October 2014
• Authors: Wen Ming Guo, Liang Sun
• Keywords: Collaborative Filtering, IPTV, Latent Context, Personalized Recommendation

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