Personalized fitting recommendation based on support vector regression

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

Research output: Contribution to journalArticlepeer-review

18 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number21
JournalHuman-centric Computing and Information Sciences
Volume5
Issue number1
DOIs
Publication statusPublished - 2015 Dec 29

Keywords

  • Collaborative filtering
  • Deviation adjustment
  • Personalized fitting
  • Recommendation

ASJC Scopus subject areas

  • Computer Science(all)

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