基于改进贝叶斯概率模型的推荐算法  

Improved Bayesian Probabilistic Model Based Recommender System

作　　者：刘付勇[1] 高贤强[1] 张著[1] 

机构地区：[1]塔里木大学信息工程学院,阿拉尔843300

出　　处：《计算机科学》2017年第5期285-289,共5页Computer Science

基　　金：国家科技支撑计划(2013BAH27F00);塔里木大学校长基金项目(TDZKQN201616);新疆南疆农业信息化研究中心项目(TSAI201402)资助

摘　　要：针对现有基于矩阵分解的协同过滤推荐系统预测精度与推荐精度较低的问题,提出一种改进的矩阵分解方法与协同过滤推荐系统。首先,将评分矩阵分解为两个非负矩阵,并对评分做归一化处理,使其具有概率语义;然后,采用变分推理法计算贝叶斯概率模型实部后验的分布;最后,搜索相同偏好的用户分组并预测用户的偏好。此外,基于用户向量的稀疏性设计一种低计算复杂度、低存储成本的推荐结果决策算法。基于3组公开数据集的实验结果表明,本算法的预测性能以及推荐系统的效果均优于其他预测算法与推荐算法。Aiming at the problem that matrix factorization based collaborative filtering recommender systems perform low accuracy in prediction and recommendation, a improved matrix factorization method and coUabomtive filtering recom- mender system were proposed. Firstly, the rating matrix is factorized into two non-negative matrices, and the rating re- sults are normalized to show probabilistic meaning. Then, variational inference is used to compute the distribution of the real posterior distribution of Bayesian model. Lastly, the user groups with the same preference are searched and the preferences of each user are predicted. Besides, a recommendation result decision algorithm with low computational com- plexity and low storage overhead was designed based on the sparsity of the user vectors. Three public datasets based ex- perimental results show that the proposed algorithm has better performance than other algorithms in prediction accura- cy and recommendation effect.

关 键 词：协同过滤 贝叶斯概率模型 变分推理 矩阵分解 评分矩阵 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]