基于评分子空间和信任机制的协同过滤推荐算法  

Collaborative Filtering Method Based on User Rating Subspace and Trust Mechanism for Recommendation System

作　　者：张莉[1] 孙晓寒 郑晓晗 Zhang Li;Sun Xiaohan;Zheng Xiaohan(School of Computer Science and Technology,Soochow University,Suzhou 215006,China)

机构地区：[1]苏州大学计算机科学与技术学院,江苏苏州215006

出　　处：《南京师范大学学报（工程技术版）》2023年第3期27-35,共9页Journal of Nanjing Normal University(Engineering and Technology Edition)

基　　金：江苏省高校自然科学研究项目(19KJA550002);江苏省六大人才高峰项目(XYDXX-054);江苏高校优势学科建设工程资助项目.

摘　　要：互联网技术的快速发展导致了互联网上数据信息的爆炸式增长.推荐系统作为解决互联网信息过载问题的关键技术,其核心思想是通过用户历史行为数据挖掘出用户的个性化偏好,为用户推荐其感兴趣的物品.然而,稀疏的评分数据会导致相似度计算不够准确,进而影响相似用户集的质量.为了提高相似用户搜索的可靠性,引入信任机制和评分子空间,提出基于评分子空间和信任机制的协同过滤推荐算法.创新点主要包括以下两点:首先,算法引入基于用户显式声明的关系数据所构建的信任机制,该关系数据能够对稀疏的评分数据进行补充.其次,利用评分子空间和信任关系,设计了一种基于隐式和显式相似度的混合相似度度量方式,并将之引入到多阶近邻的相似用户搜索方法和迭代评分预测方案中.实验结果表明,所提算法提高了推荐的准确度,具备较好的预测能力.The rapid growth of the internet has led to the explosive growth of information on the internet.To solve the issue of information overload,recommendation system has been proposed.The core idea behind recommendation system is to explore users’personalized preferences based on users’historical behavior data,and recommend items that match users’interests to users.However,sparse rating data leads to poor accurate similarity calculations,which in turn affect the quality of similar user sets.To improve the reliability of similar user set,this paper proposes a collaborative filtering algorithm based on user rating subspace and trust mechanism(URSTM)for recommendation system.The innovation of this paper contains the following two main points.Firstly,URSTM introduces a trust mechanism constructed based on a trust relationship explicitly declared by users,which can supplement the sparse rating data.Secondly,by using the rating subspace and trust relationship,this paper designs a hybrid similarity measurement based on explicit and implicit similarities,and then integrates it into the multi⁃order nearest neighbor search method and iterative rating prediction method.Experimental results show that URSTM can improve the accuracy of recommendation performance and has a better prediction ability.

关 键 词：推荐系统 协同过滤 信任机制 用户评分子空间 迭代评分预测 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]