融合双聚类技术的新型协同过滤算法

A Novel Collaborative Filtering Algorithm Using Biclustering

针对传统协同过滤算法因未考虑用户与项目之间的对偶性而产生的数据稀疏问题,提出一种基于双聚类技术和Top-n推荐技术的新型协同过滤算法:通过同时对行之间和列之间进行聚类,可以对用户和项目之间的相似性进行辨识,从而确定二者的分组信息;利用Top-n算法快速、精确地搜索出最佳的服务推荐.为了验证所提方法的有效性,在网络服务数据集上进行了相关实验.实验结果表明提出的方法能够在存在稀疏数据的情况下为用户提供有效的推荐,提高网络服务推荐的搜索精确性,提升CPU的有效利用率,并大幅度增强了算法的鲁棒性.

In order to deal with the data sparsity of traditional collaborative filtering algorithm（CFA）caused by the ignorance of the existent duality between users and items,a novel CFA based on biclustering technique and Top-nrecommendation is proposed.By clustering the rows and the columns at the same time,the similarities between users and items can be identified,and the grouped information of user-item can be obtained.The Top-nalgorithm is used to search the optimal recommendation fast and precisely.To validate the effectiveness of the proposed method,the Web Service Dataset is used to take some experiments.The results show that the proposed method can provide useful recommendations for the users in the presence of sparse data,enhance the searching precision of web service recommendation,increase the efficiency of CPU resource,and the robustness is improved substantially.