LDA模型和列表排序混合的协同过滤推荐算法  

Collaborative Filtering Recommendation Algorithm Mixing LDA Model and List-wise Model

作　　者：王涵 夏鸿斌[1,2] WANG Han;XIA Hong-bin(School of Digital Media,Jiangnan University,Wuxi,Jiangsu 214122,China;Key Laboratory of Media Design and Software Technology of Jiangsu Province,Jiangnan University,Wuxi,Jiangsu 214122,China)

机构地区：[1]江南大学数字媒体学院,江苏无锡214122 [2]江南大学江苏省媒体设计与软件技术重点实验室,江苏无锡214122

出　　处：《计算机科学》2019年第9期216-222,共7页Computer Science

基　　金：国家科学支撑计划课题(2015BAH54F01)资助

摘　　要：基于排序学习的协同过滤推荐算法受数据稀疏性的影响,出现了推荐不准确性的问题。为此,文中提出了一种结合LDA主题模型和列表排序的混合排序学习协同过滤算法。该算法首先使用LDA主题模型对用户-项目评分矩阵建模,获取用户潜在低维主题向量来度量用户之间的相似度;然后通过列表排序学习函数为用户直接预测满足其偏好的排序列表。在Movielens和EachMovie两个真实数据集上的实验结果表明:该算法可以避免排序学习算法由于用户间共同评分信息过少引起的相似度计算不准确的问题,同时体现出了排序推荐的优越性,有效缓解了数据稀疏性带来的影响,提高了推荐准确度。Rranking-oriented collaborative filtering is affected by the sparsity of data,which leads to the inaccuracy of recommendations.This paper proposed a hybrid ranking-oriented collaborative filtering algorithm based on LDA topic model and list-wise model.The algorithm uses the LDA topic model to model the user-item ratings matrix,and obtains the potential low- dimensional topic vector of the user,then measures the similarity between users with the topic vector.Next,the list-wise learning function is used to directly predict the total order of items that satisfies the users preference.The experimental results on the two real datasets of Movielens and EachMovie show that the algorithm can avoid the inaccuracy of similarity calculation between users caused by too little common score information,and at the same time reflect the superiority of learning to rank.It can effectively alleviate the effect of data sparsity and improve the accuracy of recommendation.

关 键 词：协同过滤 排序学习 列表排序 LDA主题模型 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]