doi:10.16186/j.cnki.1673-9787.2019120071

Received:2019/12/22

Revised:2020/10/27

Published:2022/03/15

Fusion recommendation algorithm based on user attributes and item attributes

SHEN Yanmei, LI Yaping, WANG Yan

College of Computer Science and Technology, Henan Polytechnic University, Jiaozuo 454000 , Henan, China

Abstract: Traditional collaborative filtering algorithm has the problems of cold start and low recommendation accuracy.A collaborative filtering algorithm based on user attributes,a collaborative filtering algorithm based on item attributes and fusion recommendation algorithm of the former two,were proposed.When calculating user similarity,the user’s age,gender and occupational attribute differences were proposed and weighted with Pearson correlation coefficient.When calculating item similarity,the item type labels and item scoring time were proposed,and the two were combined with the project cosine.Finally,the recommendation results of the above two algorithms were weighted and fused.The experimental results showed that the improved fusion recommendation algorithm had better recommendation results in terms of mean absolute error( MAE) and time performance than the other four algorithms,and could be used in situations where new users and new items appeared.This significantly improved the recommendation quality of the recommendation system.

Key words:collaborative filtering;recommendation system;cold-start;similarity;fusion algorithm

 基于用户属性和项目属性的融合推荐算法_申艳梅.pdf