JSCN: Joint Spectral Convolutional Network for Cross Domain Recommendation

--Cross-domain recommendation can alleviate the data sparsity problem in recommender systems. T o transfer the knowledge from one domain to another, one can either utilize the neighborhood information or learn a direct mapping function. However, all existing methods ignore the high-order connectivity information in cross-domain recommendation area and suffer from the domain-incompatibility problem. In this paper, we propose a Joint Spectral Convolutional Network (JSCN) for cross-domain recommendation. JSCN will simultaneously operate multi-layer spectral convolutions on different graphs, and jointly learn a domain-invariant user representation with a domain adaptive user mapping module. As a result, the high-order comprehensive connectivity information can be extracted by the spectral convolutions and the information can be transferred across domains with the domain-invariant user mapping. The domain adaptive user mapping module can help the incompatible domains to transfer the knowledge across each other . Extensive experiments on 24 Amazon rating datasets show the effectiveness of JSCN in the cross-domain recommendation, with 9 .2% Recommending users with a set of preferred items is still an open problem [1]-[6], especially when the dataset is very sparse. To remedy the data sparsity issue, broad-leraning based model [7] and cross-domain recommender system [4], [8] are proposed where the information from other source domains can be transferred to the target domain. To transfer the knowledge from one domain to another, one can use the overlapping users [4], [6], [8], [9] in two ways: (1) the neighborhood information of common users stores the structure information of different domains with which we can do cross-domain recommendation [6], [10]; or (2) we can learn a mapping function [4], [8] to project latent vectors learned in one domain into another, and thus the knowledge can be transferred.