基于多关系社交网络的协同过滤推荐算法

Collaborative Filtering Recommendation Algorithm Based on Multi-relationship Social Network

推荐系统是大数据中最常见的应用之一,传统的协同过滤推荐算法直接基于用户-项目评分矩阵,对于海量的用户和商品数据,算法的执行效率将会显著降低。针对这一问题,提出了一种基于多关系社交网络的协同过滤推荐算法。该算法利用信息传播方法对基于多子网复合复杂网络模型构建的多关系社交网络进行社团结构划分,从而将相似度接近的用户划分到一个社团中,进而在社团内部选择用户的k-近邻集合来构建用户-项目评分矩阵,然后利用协同过滤算法进行推荐,从而实现了在不降低推荐准确率的前提下提升推荐算法的执行效率。在真实数据集Epi-nions上,将所提算法与传统的协同过滤推荐算法进行对比。实验结果表明,所提算法具有较高的推荐效率和准确率,特别是对于海量数据,推荐算法的执行时间缩短到原有的1/10。

Recommendation system is one of the most common applications in big data.Traditional collaborative filtering recommendation algorithm is directly based on user-item scoring matrix.For massive user and commodity data,the efficiency of the algorithm will be significantly reduced.Aiming at this problem,this paper proposed a collaborative filtering recommendation algorithm based on multi-relational social network.The information propagation method is used to detect communities in the multi-relationship social network based on multi-subnet composite complex network model,the users with similarity are divided into the same community.And then the k-nearest neighbor set of users is selected to construct the user-item scoring matrix within the community.Then the collaborative filtering algorithm is used to recommend through the new user-item scoring matrix,thus improving the efficiency of recommendation algorithm without reducing the accuracy of recommendation.Compared with traditional collaborative filtering recommendation algorithm on real data set Epinions,the results show that the proposed algorithm has high recommendation efficiency and accuracy.Especially for big data,the execution time of the proposed recommendation algorithm is improved by more than 10 times.