Personalized Video Recommendations with Both Historical and New Items

Recommender systems have been proven as an essential tool to solve the information overload problem due to the burst of Internet traffic, however traditional approaches only consider to recommend items that users have not seen before, and thus ignore the significance of those items in a user’s historical records. This is motivated by the fact that users often revisit those items they have watched before, especially for TV series. Based on this, in this paper, we introduce a new concept called “revisiting ratio”, to uniquely represent the ratio between the new and old items. We also propose a “preference model” to aid selecting the most related historical records. Finally, theoretical analysis and extensive results are supplemented to show the advantages of the proposed system.

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