基于多源信息聚类和IRC-RBM的混合推荐算法.

To solve the problem of data sparsity in collaborative filtering，this paper proposes a hybrid recommendation algorithm combining multi-source information clustering and IRC-RBM. Firstly， this algorithm takes user trust and project time weight as the clustering basis，uses the K-means clustering algorithm of minimum spanning tree to carry out clustering analysis on users generates K similar user sets，and conducts scoring prediction on the basis of clustering analysis. Finally，the scoring matrix after clustering and the scoring matrix generated by IRC-RBM model are weighted and fused by linear weighting and Top-N is used for recommendation. Experimental results show that the proposed hybrid recommendation algorithm significantly improves the accuracy in comparison to the traditional recommendation algorithm. [ABSTRACT FROM AUTHOR]