申艳梅, 李亚平, 王岩.基于用户属性和项目属性的融合推荐算法[J].河南理工大学学报（自然科学版）,2022,41(2):131-137.

SHEN Y M, LI Y P, WANG Y. Fusion recommendation algorithm based on user attributes and item attributes[J].Journal of Henan Polytechnic University(Natural Science) ,2022,41(2):131-137.

基于用户属性和项目属性的融合推荐算法

申艳梅, 李亚平, 王岩

河南理工大学 计算机科学与技术学院,河南 焦作454000

摘要:针对传统协同过滤算法的冷启动、推荐精度低等问题,提出基于用户属性和项目属性的协同过滤算法以及它们两者的融合推荐算法。在计算用户相似度时,提出用户年龄、性别和职业属性差异度,并与皮尔逊相关系数加权结合；在计算项目相似度时,提出项目类型标签和项目被评分时间,并将两者与项目余弦相似度融合。最后将上述两种算法的推荐结果进行加权融合。实验结果表明,改进的融合推荐算法相比其他4种算法在平均绝对误差率（mean absolute error,MAE）和时间性能方面有更好的推荐结果,并且能够在有新用户和新项目出现的情况下明显提高推荐系统的推荐质量。

关键词:协同过滤;推荐系统;冷启动;相似度;融合算法

doi:10.16186/j.cnki.1673-9787.2019120071

基金项目:国家自然科学基金资助项目（61502150）；河南理工大学博士基金资助项目（B2015-42）；河南省高等学校重点科研项目 （16A120013）

收稿日期:2019/12/22

修回日期:2020/10/27

出版日期: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