Multi-Layer Transfer Learning for Cross-Domain Recommendation Based on Graph Node Representation Enhancement

Xin Ni, Jie Nie, Niantai Jing, Jianliang Xu, Xiaodong Wang, Xuesong Gao, Mingxing Jiang, Chi-Hung Chi, Zhiqiang Wei

Abstract: Effectively representing and transferring user preferences across various domains presents a significant challenge in cross-domain recommendation (CDR). Some approaches utilize graph neural networks that use interaction behavior to establish relationships between entities, providing a comprehensive understanding of user interests. However, the impact of consistent semantics across various types, fields, and perspectives of social media information on user preferences is overlooked, i.e. the multidimensional consistency of user preferences. This oversight results in graph node representations that inadequately reflect user preferences. To address these limitations, we propose a multi-layer transfer learning network (MTLG) for CDR based on graph node representation enhancement via multi-dimensional consistent user preferences. Firstly, the model introduces a set of globally shared semantic units to perform different-grained semantic alignment of multiple media information without clear alignment boundaries, thereby modeling multi-dimensional consistent user preference features. These features are then seamlessly integrated with the initial high-order graph structure embedding features, thus significantly improving the quality of graph node representation. Secondly, the model innovatively designs a multi-layer transfer learning network that hierarchically aligns the domain distribution differences. It calculates the similarity between domains to derive layer weights for more precise transfer learning, thereby mitigating the possibility of information error accumulation resulting from inaccurate feature aggregation processes. We conducted numerous experiments on 3 scenarios, including 7,954,943 rating information from the Amazon dataset. The results indicate that MTLG’s recommendation accuracy surpasses those of state-of-the-art methods.

External IDs:dblp:journals/tmm/NiNJXWGJCW25