基于评分偏好和项目属性的协同过滤算法

doi:10.11896/jsjkx.190300056

Abstract

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

CLC Number:

TP311

ZHU Lei, HU Qin-han, ZHAO Lei, YANG Ji-wen. Collaborative Filtering Algorithm Based on Rating Preference and Item Attributes[J].Computer Science, 2020, 47(4): 67-73.

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