关键词: 协同过滤算法, 反馈信息, 主成分分析, 张量分解, 推荐算法

Abstract: Recommendation algorithm based on tensor factorization has low accuracy and data sparseness problem.Therefore,on the basic of the traditional tensor decomposition model,this paper introduces the user nearest neighbor information,and proposes N dimensional tensor decomposition model.Using context aware information,it uses implicit feedback information as the third dimension to establish N dimensional tensor decomposition model.To further improve the the quality of recommendation,it adds the user nearest neighbor information to optimize the N dimensional tensor decomposition model to improve the accuracy of the tensor decomposition recommendation algorithm.Experimental results show that the tensor decomposition recommendation algorithm combined with user nearest neighbor has better accuracy than the traditional tensor decomposition algorithm,can effectively solve the sparsity and accuracy problems.

Key words: collaborative filtering algorithm, feedback information, Principal Component Analysis(PCA), tensor decomposition, recommendation algorithm