MF-GSLAE: A Multi-Factor User Representation Pre-Training Framework for Dual-Target Cross-Domain Recommendation

作者：Hao Wang, Mingjia Yin, Luankang Zhang, Sirui Zhao, Enhong Chen · 发表于：ACM Transactions on Information Systems · 年份：2024 · DOI：10.1145/3690382 · 被引用次数：5 · 研究领域：Recommender Systems and Techniques、Topic Modeling、Machine Learning in Healthcare

Recently, the dual-target cross-domain recommendation has been an emerging research problem, which aims to improve the performances of both source and target domains by transferring the preferences of overlapping users. Most of the existing work adopted a coarse-grained manner to detach general users’ preferences and associate them with domain-specific information for enhancing user representation learning, which fails to depict the differences in users’ diverse preferences and aggregate relevant preferences with improper propagation. To this end, in this article, we propose a multi-factor user representation pre-training framework, dubbed MF-GSLAE, with a focus on fine-grained preference learning and transferring. Specifically, we first propose a fine-grained factor representation pre-training paradigm. It projects the behavior records of both domains into several subspaces and introduces a compactness regularization to generate multiple fine-grained preference factors. Furthermore, we propose a multi-factor graph structure learning method within linear complexity to efficiently construct preference connections on different scales of users, which could aggregate the intrinsic relationship of user preferences in immediate embedding spaces to capture high-order information. Following the pre-training, we subsequently design a factor selection module with the bootstrapping mechanism to adaptively choose the corresponding domain-related preferences and transfer domain-shared inf...