计算机应用研究2016，Vol.33Issue(11)：3240-3244,5.DOI:10.3969/j.issn.1001--3695.2016.11.010
基于信任和概率矩阵分解的协同推荐算法研究
Research on collaborative filtering recommendation algorithm based on trust and probabilistic matrix factorization
摘要
Abstract
To overcome the problem of cold-start and data sparsity in collaborative filtering recommender systems,the resear-chers utilized the trust relationship between users to propose a variety of trust-based recommender algorithms.Though they im-proved the recommender coverage,the recommender precision came down.So this paper took the users’influence and the latent factors into account,proposed a trust-based and probabilistic matrix factorization for collaborative filtering recommendation algo-rithm.First,the algorithm integrated the knowledge of the users’trust,similitude specialty,and so on,calculated asymmetrical trust value between users.Then it fused the probabilistic matrix factorization method to predict the ratings.Finally,it experi-mented on the real dataset.And the result shows that this algorithm can effectly improve accuracy of rating prediction.关键词
推荐系统/协同过滤/信任/数据稀疏/冷启动/矩阵分解Key words
recommender systems/collaborative filtering/trust/data sparsity/cold-start/matrix factorization分类
信息技术与安全科学引用本文复制引用
郑修猛,陈福才,柯丽虹..基于信任和概率矩阵分解的协同推荐算法研究[J].计算机应用研究,2016,33(11):3240-3244,5.基金项目
国家自然科学基金资助项目（61171108）；国家“973”计划资助项目（2012CB315901，2012CB315905）；国家科技支撑计划资助项目 （）
