A New Similarity Computing Model of Collaborative Filtering

被引：13

作者：

Jin, Qibing ^{[1
]}

Zhang, Yue ^{[1
]}

Cai, Wu ^{[1
]}

Zhang, Yuming ^{[1
]}

机构：

[1] Beijing Univ Chem Technol, Coll Informat Sci & Technol, Beijing 100029, Peoples R China

来源：

IEEE ACCESS | 2020年 / 8卷

基金：

中国国家自然科学基金;

关键词：

Recommender system; collaborative filtering; context information; RECOMMENDATION; ITEM;

D O I：

10.1109/ACCESS.2020.2965595

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Collaborative filtering has become one of the most widely used methods for a variety of commercial recommendations. The key to collaborative filtering is use similarity calculation formula to find similar neighbors or projects. However, most similarity calculation methods only use the user common score and provide bad recommendations. This paper proposes a new similarity measure method, which effectively utilizes the user context information. The new method uses a singularity factor to adjust nonlinear equation and takes into account the user scoring habits. It can improve the accuracy of the prediction. The new method has been tested on the dataset and compared with other algorithms. The results show that the proposed method can improve the recommendation quality.

引用

页码：17594 / 17604

页数：11

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