Research on Collaborative Recommendation Method Based on Multiple Data Sources of Social Network - Ruiqin Wang; Jun Pan; Yixiao Li (Dianxin kexue)
As an effective recommendation method，collaborative filtering typically has the data sparsity and cold-start problems. It was proposed that using multiple data sources of social network to overcome the above problems. First of a11，both the rating similarity and the social trust between users were considered to resolve the data sparsity problem. Then a simple and effective trust reasoning method was proposed to identify the implicit trust relationship between users. In order to solve the cold-start problem，information of the category of items and domain experts was used for joint recommendation. Experimental results show that the proposed algorithm has significantly better precision and reca11 than existing methods.