A Three-Layer Attentional Framework Based on Similar Users for Dual-Target Cross-Domain Recommendation

Lü, Jinhu; Fang, Xiu Susie; Yang, Jian; Wang, He

doi:10.1007/978-3-031-30672-3_20

Cited by 3 publications

(1 citation statement)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As a promising direction, cross-domain recommendation (CDR) has attracted a surge of investigations, which enables the effective learning of a data-sparser domain by transferring useful knowledge from data-richer domains. Existing CDR methods often assume the existence of shared information so that a mapping function can be learned across different domains [27,31,39,58] or availability of source and target domains for joint optimization [20,23,56]. However, this assumption may not hold in real-world applications due to the considerable gap between source and target domains or even unavailability of target domains data during training, which severely hinders the application of existing CDR approaches.…”

Section: Introductionmentioning

confidence: 99%

PEACE: Prototype lEarning Augmented transferable framework for Cross-domain rEcommendation

Gan,

Huang,

et al. 2024

Proceedings of the 17th ACM International Conference on Web Search and Data Mining

View full text Add to dashboard Cite

To help merchants/customers to provide/access a variety of services through miniapps, online service platforms have occupied a critical position in the effective content delivery, in which how to recommend items in the new domain launched by the service provider for customers has become more urgent. However, the non-negligible gap between the source and diversified target domains poses a considerable challenge to cross-domain recommendation systems, which often leads to performance bottlenecks in industrial settings.While entity graphs have the potential to serve as a bridge between domains, rudimentary utilization still fail to distill useful knowledge and even induce the negative transfer issue. To this end, we propose PEACE, a Prototype lEarning Augmented transferable framework for Cross-domain rEcommendation. For domain gap bridging, PEACE is built upon a multi-interest and entity-oriented pre-training architecture which could not only benefit the learning of generalized knowledge in a multi-granularity manner, but also help leverage more structural information in the entity graph. Then, we bring the prototype learning into the pre-training over source domains, so that representations of users and items are greatly improved by the contrastive prototype learning module and the prototype enhanced attention mechanism for adaptive knowledge utilization. To ease the pressure of online serving, PEACE is carefully deployed in a lightweight manner, and significant performance improvements are observed in both online and offline environments. CCS CONCEPTS• Information systems → Recommender systems.

show abstract