Computer Science ›› 2020, Vol. 47 ›› Issue (11A): 471-473.doi: 10.11896/jsjkx.200600109

• Big Data & Data Science • Previous Articles     Next Articles

Balance Between Preference and Universality Based on Explicit Feedback Collaborative Filtering

HUANG Chao-ran, GAN Yong-shi   

  1. Department of Computer Science,Hong Kong Baptist University,Hong Kong 999077,China
  • Online:2020-11-15 Published:2020-11-17
  • About author:HUANG Chao-ran,born in 1995,postgraduate.His main research interests include data mining and social network analysis.

Abstract: Collaborative filtering (CF) based on explicit feedback only exists three variables,and its similarity computing method depends on the explicit feedback of user's rating data,but never considers the implicit factors existed in the real word's recommendation,which determines that CF is limited in mining the preference of users and items,but it lacks of the abilities of mining the universality of users and items[5].Academia has proposed various of innovative ideas to improve the traditional CF,but most of improvements are vertical improvements for CF algorithm like adding the mechanisms of classification,clustering and time series to the algorithm,which improve the algorithm structure but barely improve the variable factors.Therefore,it still cannot mining the universality of users and items deeply.This paper proposes a horizontal improvement:Collaborative Filtering & Regression Weighted Average (CRW),intending to mine the universality of users and items through tree regression while keeping the preference of users and items through CF,and conducting weighted average between the predicting result of regression and CF,in order to balance the strength of preference and the weakness of universality of CF.Experiment result shows that with a proper weighting coefficient a,the mean square error of predicting result of CRW is distinctly lower than that of CF and regression,which shows CRW performs better than single CF and regression.

Key words: Collaborative filtering, Explicit feedback, Tree regression, Recommended system, Preference and universality

CLC Number: 

  • TP391.3
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