Open AccessImproved collaborative filtering recommendation algorithm based on user attributes and K-means clustering algorithm
Author(s) -
Lihong Chen,
Yi Luo,
Xudong Li,
Weijie Wang,
Ming Ni
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1903/1/012036
Subject(s) - collaborative filtering , cluster analysis , computer science , similarity (geometry) , recommender system , algorithm , data mining , k means clustering , machine learning , artificial intelligence , image (mathematics)
Aiming at the problem of poor performance of collaborative filtering algorithm on data sets with large sparsity, this paper proposes an improved collaborative filtering recommendation algorithm which integrates user attributes and K-means clustering. When considering user similarity, the weight of user attributes is introduced to reduce the impact of data sparsity on similarity calculation. Meanwhile, the characteristics of user’s age, gender and occupation are concerned. At the same time, combined with K-means clustering, the algorithm can further improve the accuracy of the recommendation model.