文献类型：期刊文献

中文题名：基于信任关系下用户兴趣偏好的协同过滤推荐算法

英文题名：Collaborative Filtering Recommendation Algorithm Based on User Preference Under Trust Relationship

作者：邵超[1];宋淑米[1]

第一作者：邵超

机构：[1]河南财经政法大学计算机与信息工程学院,郑州450046

第一机构：河南财经政法大学计算机与信息工程学院

年份：2021

卷号：48

期号：S01

起止页码：240-245

中文期刊名：计算机科学

外文期刊名：Computer Science

收录：CSTPCD;;北大核心:【北大核心2020】；CSCD:【CSCD_E2021_2022】；

基金：国家自然科学基金(61202285,61502146,61841702)。

语种：中文

中文关键词：协同过滤推荐;聚类;偏好矩阵;信任关系;矩阵分解;相似度;

外文关键词：Collaborative filtering recommendation;Clustering;Preference matrix;Trust relationship;Matrix factorization;Similarity;

摘要：随着信息的海量增长,推荐系统有效缓解了信息爆炸带来的问题,其中协同过滤作为主流技术之一受到了广泛的关注。针对用户的兴趣偏好研究主要是基于商品标签的有监督数据集进行研究,忽略了无监督数据集,同时,在计算用户的兴趣偏好过程中也未能考虑到信任用户对用户兴趣的影响。为此,文中首先在无监督的项目数据集上采用矩阵分解模型得到项目的潜在特征向量,据此对项目进行聚类以表示项目的类别信息;然后,结合用户的信任关系和用户-项目评分矩阵构造用户的兴趣偏好矩阵;最后,为提高推荐效率,在用户的兴趣偏好矩阵上对用户进行聚类,在每个聚类簇内计算用户之间的相似度,从而实现推荐。在公开数据集上的实验结果表明,该算法能有效改善推荐结果的精确性,提升推荐质量。
With the massive increase of information,the recommendation system has effectively alleviated the problems caused by the information explosion.Collaborative filtering,as one of the mainstream technologies of recommendation system,has been widely concerned.In the research of users’interest preference,the supervised data sets based on commodity labels are mainly studied,and the unsupervised data sets are ignored.At the same time,the influence of trusted users on users’interest is not considered in the process of calculating users’interest preference.To solve these problems,a collaborative filtering recommendation algorithm based on user preference under trust relationship is proposed in this paper.Firstly,the potential feature information of the items is obtained using the matrix factorization(MF)model,and then is clustered to obtain item type information.Secondly,the user trust relationship and users-item rating information are considered to construct the user preference matrix.Finally,the users are clustered based on the user preference matrix,and then the similarities between users in one cluster are calculated to implement recommendation.Experimental results on open datasets show that the algorithm can effectively improve the accuracy of recommendation results and the quality of recommendations.