嵌入LDA主题模型的协同过滤推荐算法

协同过滤推荐算法由于其推荐的准确性和高效性已经成为推荐领域最流行的推荐算法之一。该算法通过分析用户的历史评分记录来构建用户兴趣模型,进而为用户产生一组推荐。然而,推荐系统中用户的评分记录是极为有限的,导致传统协同过滤算法面临严重的数据稀疏性问题。针对此问题,提出了一种改进的嵌入LDA主题模型的协同过滤推荐算法(ULR-CF算法)。该算法利用LDA主题建模方法在用户项目标签集上挖掘潜在的主题信息,进而结合文档-主题概率分布矩阵和评分矩阵来共同度量用户和项目相似度。实验结果表明,提出的ULR-CF算法可以有效缓解数据稀疏性问题,并能显著提高推荐系统的准确性。

Topic Model Embedded in Collaborative Filtering Recommendation Algorithm

Collaborative filtering(CF) recommendation algorithm has become one of the most popular algorithms in the field of recommendation due to its accuracy and efficiency.CF algorithm constructs interest models of users through analyzing their history rating records.Then it generates a set of recommendations for users.While the rating records of users in the recommendation system are limited,it results in the traditional CF algorithm facing with serious problem of data sparsity.Therefore,to address the problem of sparsity,we proposed an improved collaborative filtering recommendation algorithm that embeds the LDA topic model,named LDA-CF.This algorithm utilizes LDA topic model method to discover latent topics information in tags of users and items.Then it unifies both the document-topic probability distribution matrix and rating matrix simultaneously to measure the similarities between users and items.The experiment results indicate that the developed ULR-CF algorithm can alleviate the sparsity problem,and improve the accuracy of recommendation system simultaneously.