融合本体语义与用户属性的协同过滤算法  

Collaborative Filtering Algorithm Combining Ontology Semantics and User Attribute

作　　者：王光[1] 姜丽 董帅含 李丰 WANG Guang;JIANG Li;DONG Shuaihan;LI Feng(School of Software,Liaoning Technical University,Huludao,Liaoning 125105,China)

机构地区：[1]辽宁工程技术大学软件学院

出　　处：《计算机工程》2019年第10期215-220,共6页Computer Engineering

基　　金：国家自然科学基金(71371091);国家自然科学基金青年科学基金(61401185)

摘　　要：传统协同过滤推荐算法在处理海量数据时存在数据稀疏性和项目长尾效应,导致推荐精度较低。针对该问题,结合本体语义和用户属性,提出一种改进的协同过滤算法。利用本体计算项目之间的语义相似度,构建项目相似度矩阵,同时引入用户属性计算用户相似度矩阵。通过融合本体语义和用户属性形成用户-项目评分矩阵,并对该矩阵的预测评分进行加权处理,生成TOP-N推荐结果。实验结果表明,相比传统皮尔逊相似度计算协同过滤算法、基于本体语义的协同过滤算法和基于评分矩阵填充与用户兴趣的协同过滤算法,该算法的平均绝对误差较低,准确率较高,综合性能及新颖度较优。When dealing with massive data,the traditional collaborative filtering recommendation algorithm has the data sparsity and the long tail effect of the items,resulting in low recommendation accuracy.Aiming at this problem,combining ontology semantics and user attributes,this paper proposes an improved collaborative filtering algorithm.The item similarity matrix is constructed by using ontology to calculate semantic similarity between items.User attributes are introduced to calculate user similarity matrix.The user-item scoring matrix is formed by integrating ontology semantics and user attributes,and the prediction score of the matrix is weighted to provide the TOP-N recommendation results.Experimental results show that compared with Pearson similarity calculation collaborative filtering algorithm,collaborative filtering algorithm based on ontology semantics,and collaborative filtering algorithm based on scoring matrix filling and user interest,the proposed algorithm has the lowest mean absolute error and the highest precision,and its integrity and novelty are superior.

关 键 词：协同过滤 本体语义 用户属性 相似度 长尾效应 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]