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

Weimin Li, Mengke Yao, Qun Jin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationMultimedia and Ubiquitous Engineering
EditorsShu-Ching Chen, James J. Park, Neil Y. Yen, Joon-Min Gil
PublisherSpringer Verlag
Pages173-178
Number of pages6
ISBN (Electronic)9783642548994
DOIs
Publication statusPublished - 2014 Jan 1
EventFTRA 8th International Conference on Multimedia and Ubiquitous Engineering, MUE 2014 - Zhangjiajie, China
Duration: 2014 May 282014 May 31

Publication series

NameLecture Notes in Electrical Engineering
Volume308
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Conference

ConferenceFTRA 8th International Conference on Multimedia and Ubiquitous Engineering, MUE 2014
CountryChina
CityZhangjiajie
Period14/5/2814/5/31

Keywords

  • Collaborative filtering
  • Deviation adjustment
  • Personalized fitting
  • Recommendation

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering

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  • Cite this

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