Cross-domain attention transfer network for recommendation

Xuan Zhang, Ruyu Yan, Yushun Fan, Jia Zhang, Hongxing Yuan, Chunyu Wei

Abstract: To address the data sparsity issue in the cold-start problem of recommendation, cross-domain recommendation techniques transfer user latent preferences by leveraging transparent data from auxiliary (source) domains to target domain. However, most methods overlook the differences of user preferences for various aspects of items. Furthermore, in real-world scenarios, due to data privacy concerns, data sharing among domains is impractical in most cases. This paper proposes a novel Cross Domain Attention Transfer Network (CDATN) for recommending items to cold-start users in target domains, aiming to solve both problems. CDATN employs a fine-grained user preference aggregation module to achieve accurate user preference transfer. To address user privacy concerns, CDATN adopts a cautious approach by exclusively leveraging user embeddings that are derived from the auxiliary domain. A meticulously designed two-step training strategy is employed to guarantee alignment between these user embeddings and the latent space of user preferences. This dual-process not only caters effectively to the recommendation requirements of both active and cold-start users but also improves the overall efficacy of the recommendation system. Extensive experiments on real-world datasets have shown that the proposed CDATN framework surpasses state-of-the-art baselines in recommendation for cold-start users.

External IDs:dblp:journals/aei/ZhangYFZYW25