Personalized fitting with deviation adjustment based on support vector regression for recommendation

Weimin Li, Mengke Yao, Qun Jin

研究成果: Conference contribution

抜粋

Almost all of the existing Collaborative Filtering (CF) methods rely only on the rating data while ignoring some important implicit information in non-rating properties for users and items, which have a significant impact on the preference. In this study, considering that the average rating of users and items have a certain stability, we firstly propose a personalized fitting pattern to predict missing ratings based on the trusty score set, which combines both the user-based CF 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 (SVR). Experiment results show that our proposed algorithms can increase the accuracy of recommendation versus the traditional CF.

元の言語English
ホスト出版物のタイトルMultimedia and Ubiquitous Engineering
編集者Shu-Ching Chen, James J. Park, Neil Y. Yen, Joon-Min Gil
出版者Springer Verlag
ページ173-178
ページ数6
ISBN(電子版)9783642548994
DOI
出版物ステータスPublished - 2014 1 1
イベントFTRA 8th International Conference on Multimedia and Ubiquitous Engineering, MUE 2014 - Zhangjiajie, China
継続期間: 2014 5 282014 5 31

出版物シリーズ

名前Lecture Notes in Electrical Engineering
308
ISSN(印刷物)1876-1100
ISSN(電子版)1876-1119

Conference

ConferenceFTRA 8th International Conference on Multimedia and Ubiquitous Engineering, MUE 2014
China
Zhangjiajie
期間14/5/2814/5/31

    フィンガープリント

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering

これを引用

Li, W., Yao, M., & Jin, Q. (2014). Personalized fitting with deviation adjustment based on support vector regression for recommendation. : S-C. Chen, J. J. Park, N. Y. Yen, & J-M. Gil (版), Multimedia and Ubiquitous Engineering (pp. 173-178). (Lecture Notes in Electrical Engineering; 巻数 308). Springer Verlag. https://doi.org/10.1007/978-3-642-54900-7_25