基于二分图划分联合聚类的协同过滤推荐算法

准确而积极地向用户提供他们可能感兴趣的信息或服务是推荐系统的主要任务。协同过滤是采用得最广泛的推荐算法之一,而数据稀疏的问题往往严重影响推荐质量。为了解决这个问题,提出了基于二分图划分联合聚类的协同过滤推荐算法。首先将用户与项目构建成二分图进行联合聚类,从而映射到低维潜在特征空间;其次根据聚类结果改进2种相似性计算策略:簇偏好相似性和评分相似性,并将二者相结合。基于结合的相似性,分别采用基于用户和项目的方法来获得对未知目标评分的预测。最后,将这些预测结果进行融合。实验结果表明,所提算法比最新的联合聚类协同过滤推荐算法具有更好的性能。

A collaborative filtering recommendation algorithmbased on bipartite graph partitioning co-clustering

To accurately and actively provide users with potentially interested information or services is the main task of a recommender system. Collaborative filtering is one of the most widely used recommendation algorithms, whereas it is suffering the issue of data sparsity that severely degrades recommendation quality. To address this issue, we propose a collaborative filtering recommendation algorithm based on bipartite graph partitioning co-clustering, called BPCF. Firstly, users and items are constructed into a bipartite graph for co-clustering, which is then mapped to the low-dimensional feature space. Then, the proposed algorithm computes the two types of improved similarities (cluster preference similarity and rating similarity) according to the clustering results and combines them. Based on the combined similarity, the user-based approach and item-based approach are adopted, respectively, to predict for an unknown target rating and these prediction results are fused. Experimental results show that the proposed method outperforms the state-of-the-art co-clustering collaborative filtering recommendation algorithms.