计算机工程与应用2018，Vol.54Issue(4)：77-83,7.DOI:10.3778/j.issn.1002-8331.1701-0310
融合信任关系和用户项目二部图的推荐算法
Incorporating social trust relationship and bipartite network for recommendation
摘要
Abstract
Cold-start and data sparsity issues have still been two challenges in recommender systems.In most of traditional recommender systems based on the matrix factorization model,it is often assumed that users are isolated and the relation-ships among users are ignored,this results in the decrease in the recommendation effects.Thus,a novel approach incorpo-rating social trust relationship and the structure of bipartite network is proposed. Based on the matrix factorization, this proposed approach combines the social trust relationships among users with the structure of bipartite network,and employs the gradient algorithm to train model parameters.The experimental results on Epinions data set show that the proposed approach is superior to other advanced approaches in accuracy and reliability,especially while the cold-start and data spar-sity issues are involved in.关键词
协同过滤/信任关系/矩阵分解/二部图/物质扩散Key words
collaborative filtering/trust relationship/matrix factorization/bipartite network/mass diffusion分类
信息技术与安全科学引用本文复制引用
陈平华,杨凯..融合信任关系和用户项目二部图的推荐算法[J].计算机工程与应用,2018,54(4):77-83,7.基金项目
广东省省级科技计划项目(No.2016B030308001,No.2016B030306002) （No.2016B030308001,No.2016B030306002）
广州市科技计划项目(No.201604010099). （No.201604010099）
