Personalized Fitting with Deviation Adjustment Based on Support Vector Regression for Recommendation

Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 308)

Abstract

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.

Keywords

collaborative filtering recommendation personalized fitting deviation adjustment 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.School of Computer Engineering and TechnologyShanghai UniversityShanghaiChina
  2. 2.Shanghai Key Laboratory of Computer Software Evaluating and TestingShanghaiChina
  3. 3.Colleage of Information EngineeringChina Jiliang UniversityHangzhouChina
  4. 4.Graduate School of Human SciencesWaseda UniversityTokorozawaJapan

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