6 citations · 6 across the 3 of their papers we have counts for
3 papers
cs.IR2025★ 6 cited
User Invariant Preference Learning for Multi-Behavior Recommendation
Mingshi Yan, Zhiyong Cheng, Fan Liu +2
In multi-behavior recommendation scenarios, analyzing users' diverse behaviors, such as click, purchase, and rating, enables a more comprehensive understanding of their interests,…
cs.IR2025
Federated Semantic Learning for Privacy-preserving Cross-domain Recommendation
Ziang Lu, Lei Guo, Xu Yu +3
In the evolving landscape of recommender systems, the challenge of effectively conducting privacy-preserving Cross-Domain Recommendation (CDR), especially under strict non-overlapp…
cs.IR2024
Behavior Pattern Mining-based Multi-Behavior Recommendation
Haojie Li, Zhiyong Cheng, Xu Yu +3
Multi-behavior recommendation systems enhance effectiveness by leveraging auxiliary behaviors (such as page views and favorites) to address the limitations of traditional models th…