doi:10.16186/j.cnki.1673-9787.2020.6.19

Received:2020/01/11

Revised:2020/03/22

Published:2020/11/15

Collaborative filtering recommendation algorithm based on double-factorhybrid weighted similarity

WANG Liufang1,2, LIU Zhenzhen1, WEI Lan3, WU Zhengjiang1

1.College of Computer Science and Technology，Henan Polytechnic University Jiaozuo  454000 ， Henan， China;2.Electrical Engineering Department，Hebi Automotive Engineering Professional College，Hebi  458030，Henan，China;3.School of Informatics，Xiamen University，Xiamen 361005， Fijian，China

Abstract:In the case of sparse data and cold start， the traditional similarity algorithm in collaborative filtering recommendation is used to generate the problem of inaccurate similarity. In this paper， the modified cosine similarity algorithm was weighted mixed with the similarity algorithm based on user attributes. Double-factor was introduced as the weight， and the double-factor was defined by sigmoid function， with the difference between the threshold value and the reader's borrowing quantity as the variable. When the reader borrowing was more than（less than） the threshold， the data was not sparse（sparse） ， the weight of the modified cosine similarity algorithm would automatically increase（decrease） ， and the weight of the algorithm based on user attribute similarity would automatically decrease（increase） . This method of automatically adjusting the weights of two simi larity algorithms not only considered the advantages of traditional similarity algorithms， but also avoided the disadvantages. The experiments showed that the improved algorithm had improved the accuracy of similarity calculation and improved the accuracy of recommendation， and it had solved the problems of data sparse and cold start to some extent.

Key words:recommendation algorithm;collaborative filtering;threshold;double-factor sigmoid function;weight;user attribute

基于双因子混合加权相似度的协同过滤推荐算法_王留芳.pdf