一种基于空间变换的协同过滤推荐算法

Collaborative Filtering Recommendation Algorithm Based on Space Transformation

传统的协同过滤推荐算法在实际应用中往往面临着计算可扩展性的问题。为解决此问题,文中在基于物品的协同过滤推荐的框架下,通过融合社交关系信息,提出了一种基于空间变换的协同过滤推荐算法。首先,根据用户社交网络信息,运用社区发现算法将用户划分为不同的类;其次,基于评分信息,根据用户和物品之间的对应关系找到各个用户类所对应的物品类;最后,通过各个物品对每一物品类的隶属关系,将稀疏的高维评分信息矩阵转换为一个低维稠密的物品隶属度矩阵,进而基于该矩阵进行相似度计算并进行协同过滤推荐。在公开数据集上将所提方法与其他算法进行了对比实验分析,结果表明,所提算法能够在保证推荐准确性的同时明显提升计算效率。

In real applications,traditional collaborative filtering recommendation algorithms are usually faced with the problem of computational scalability.To solve this problem,in the framework of item-based collaborative filtering recommendation,a collaborative filtering recommendation algorithm based on space transformation was proposed in this paper.Concretely speaking,according to the user social network information,the users are firstly divided into different clusters by using the community discovery algorithm.Then,item clusters are found according to the corresponding relationship between users and items in the rating information matrix.And the membership of each item for each item clusters is calculated.The sparse high dimensional rating information matrix is transformed into a low dimensional dense membership matrix,and then the similarities between items are carried on the transformed matrix.The proposed algorithm was compared with other algorithms on the public data set.The experimental results show that the proposed algorithm can significantly improve the computational efficiency while guaranteeing the accuracy of recommendation.