Computer Science ›› 2020, Vol. 47 ›› Issue (4): 67-73.doi: 10.11896/jsjkx.190300056

• Database & Big Data & Data Science • Previous Articles     Next Articles

Collaborative Filtering Algorithm Based on Rating Preference and Item Attributes

ZHU Lei, HU Qin-han, ZHAO Lei, YANG Ji-wen   

  1. Department of Computer Science and Technology,Soochow University,Suzhou,Jiangsu 215006,China
  • Received:2019-03-15 Online:2020-04-15 Published:2020-04-15
  • Contact: HU Qin-han,born in 1987,master.His main research interests include machine learning and intelligent information processing technology
  • About author:ZHU Lei,born in 1993,postgraduate.His main research interests include recommender systems and intelligent information processing technology.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (61572335),Priority Academic Program Development of Jiangsu Higher Education Institutions.

Abstract: Aiming at the impact of data sparsity of traditional collaborative filtering algorithm resulting in inaccuracy of item similarity,this paper proposed an improved collaborative filtering algorithm based on user rating preference model by incorporating time factor and item attributes.The algorithm improves the accuracy by modifying item similarity formula.Firstly,a preference model is introduced by considering the differences of user’s rating habits.A user-item rating matrix is rebuilt by replacing user’s rating of item with the preference for rating class.Then time weight function is designed and put into rating similarity based on time effect.What’s more,item similarity is calculated by incorporating item attributes similarity and rating similarity.Finally,top-N recommendation is completed after calculating user preference for item by the user preference formula.The experiment results suggest that the precision and recall of the proposed algorithm is increased by 9%~27% on the MovieLens-100K dataset and 16%~28% on the MovieLens-Latest-Small dataset than classical approaches.Therefore,the improved algorithm can improve recommendation accuracy and mitigate the problem of data sparsity effectively.

Key words: Rating preference, Time weight, Item attributes, Collaborative filtering, Similarity

CLC Number: 

  • TP311
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