Personalized fitting recommendation based on support vector regression

Weimin Li, Xunfeng Li, Mengke Yao, Jiulei Jiang, Qun Jin*


研究成果: Article査読

18 被引用数 (Scopus)


Collaborative filtering (CF) is a popular method for the personalized recommendation. Almost all of the existing CF methods rely only on the rating data while ignoring some important implicit information in non-rating properties for users and items, which has a significant impact on the preference. In this study, considering that the average rating of users and items has a certain stability, we firstly propose a personalized fitting pattern to predict missing ratings based on the similarity score set, which combines both the user-based and item-based CF. In order to further reduce the prediction error, we use the non-rating attributes, such as a user’s age, gender and occupation, and an item’s release date and price. Moreover, we present the deviation adjustment method based on the support vector regression. Experimental results on MovieLens dataset show that our proposed algorithms can increase the accuracy of recommendation versus the traditional CF.

ジャーナルHuman-centric Computing and Information Sciences
出版ステータスPublished - 2015 12 29

ASJC Scopus subject areas

  • コンピュータ サイエンス(全般)


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