四川大学学报（自然科学版）2018，Vol.55Issue(3)：477-482,6.DOI:10.3969/j.issn.0490-6756.2018.03.010
基于综合相似度迁移的协同过滤算法
Collaborative filtering algorithm based on integrated similarity transfer
摘要
Abstract
Data sparsity is one of the most challenges for traditional collaborative filtering algorithms . Transfer learning methods used the potential relationship between the target domain and the auxiliary domain to transfer the auxiliary domain knowledge ,so as to improve the recommendation accuracy of the target domain .T he existing transfer model based on similarity generally used only the rating informa-tion ,and ignores the difference of user rating .To solve these problems ,a transfer model based on com-prehensive similarity is proposed ,used user rating information and user attribute information ,taking ac-count of the difference of user rating ,used the consistency of ratings ,distribution to measure user rating similarity ,improved the accuracy of similarity computation ,thus improved the quality of data migra-tion .Experimental results showed that the proposed model can effectively alleviate the sparsity of data compared with other algorithms .关键词
数据稀疏/协同过滤/迁移学习/相似度迁移Key words
Sparse data/Collaborative filtering/Transfer learning/Similarity transfer分类
信息技术与安全科学引用本文复制引用
金玉,崔兰兰,孙界平,琚生根,王霞..基于综合相似度迁移的协同过滤算法[J].四川大学学报（自然科学版）,2018,55(3):477-482,6.基金项目
国家自然科学基金(61332006) （61332006）
