Collaborative Filtering Recommendation Based on Multi-Domain Semantic Fusion

Abstract

Collaborative filtering based on single domains has become widely used in today's recommendation system. Nevertheless, it has two problems that need to be solved, i.e., the cold start problem and the data sparseness problem. As the result, cross-domain recommendation technology has emerged, which aims at integrating user preference characteristics from different domains. This paper proposes a collaborative filtering recommendation method based on multi-domain semantic fusion (CF-MDS). CF-MDS achieves cross-domain item similarity calculation through semantic analysis and ontology and integrates data from different domains iteratively based on domain relevance to rate users on target domain items and to produce a cross-domain user-item rating matrix. Collaborative filtering technology is then combined with multi-domain fusion recommendation algorithm. Experimental results show that the proposed method can deal effectively with the cold start problem and data sparsity problem that exist in traditional recommendation systems as well as can improve the diversity of recommendation. Compared to other cross-domain recommendation methods, the proposed method can better meet personal needs of users and also improve the accuracy of recommendation.