融合信息瓶颈与图卷积的跨域推荐算法OA北大核心CSTPCD

The cross-domain recommendation based on transfer learning can effectively learn the mapping function con-necting source domain and target domain,but its performance is still affected by poor representation quality and negative transfer problem,and it can not accurately recommend users of cold start.Therefore,a cross-domain recommendation model(IBGC)combining information bottleneck and graph convolution neural network is proposed.The graph convolu-tional network is used to aggregate the associated user-user and project-item information.The attention mechanism is used to learn user and item preferences to improve the quality of node feature representation.Considering the information interaction between the two domains,three regularizers are designed to capture intra-domain and cross-domain user-item correlation by using the information bottleneck theory,and overlapping user representations in different domains are aligned to solve the negative transfer problem.Experimentsare conducted on four pairs of public datasets in the Amazon dataset.The model has performed better than the baseline model on the three recommendation performance indicators of MRR,HR@K,and NDCG@K,compared with the optimal comparison baseline model on the four datasets,MRR has improved by an average of 34.36%,HR@10 has improved by an average of 34.94%,and NDCG@10 has improved by an average of 36.83%,which proves the validity of the IBGC model.