基于用户相似性选择及标签距离的推荐算法

doi:10.3969/j.issn.0255-8297.2023.06.003

Abstract

Abstract: Neighbor selection and item label information have important influence on rating prediction of recommendation system. To improve the accuracy and scalability of recommendation systems, this paper proposes a distance-model based approach that utilizes user similarity selection and label distance. First, the users with similarity greater than the threshold value are selected as the neighbors of the users to be predicted to deal with insufficient scalability of the algorithm. Second, the user’s rating of the item is mapped to the user’s rating of the item label using the label information to enhance the accuracy. Users’ ratings of movies were predicted by using discount validation in both movie datasets. Experimental results show that the accuracy and scalability of the recommendation algorithm based on user similarity selection and label distance are greatly improved.

Key words: recommendation system, collaborative filtering, similarity screening, label distance

CLC Number:

P751.1

SU Zhan, CHEN Xueqian, AI Jun, HUANG Zhong. Recommendation Algorithm Based on User Similarity Selection and Label Distance[J]. Journal of Applied Sciences, 2023, 41(6): 940-957.

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Recommendation Algorithm Based on User Similarity Selection and Label Distance

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