Auxiliary Domain Selection in Cross-Domain Collaborative Filtering
Yi Chang, Ming-Sheng Shangand, Qian-Ming Zhang · Applied Mathematics & Information Sciences · 2015
The problem of data sparsity largely limits the accuracy of recommender systems in collaborative filtering model. To alleviate the problem, cross-domain collaborative filteri ng was proposed by harnessing the information from the auxiliary domains. Previous works mainly focused on improving the model of utilizing the auxiliary information yet little on the selection of auxiliary domains, although it is observed that the result of recommendation depends on the characteristics of auxiliary dataset . In this paper, we study the validity of cross-domain collaborative filtering by movie recommendation via different auxiliary domains of different movie genres. Through extensive experiments we find that the numbe r of overlapping users between target domain and auxiliary domain is an indicator of choosing beneficial domains, while the low Ku llback-Leibler divergence between non-overlapping user ratings, rather than the overlapping user ratings, is much more significant. The results are helpful in selection of auxiliary domains in cross-domain collaborative filtering.