3 papers
cs.IR2026
GenCAR: Generative Counterfactual Alignment with Risk-Controlled Selection for Out-of-Distribution Recommendation
Qianqian Wang, Yunshan Li, Jiawen Zeng +2
Serving useful recommendations under distribution shift is crucial for balancing utility and risk in out-of-distribution (OOD) recommendation. However, most existing OOD methods im…
cs.LG2026
ProME: Prototype-Margin Environments with Repair-Aware Selection for Group-Robust Learning
Qianqian Wang, Yunshan Li, Dawei Huang +2
Group-robust learning is crucial for maintaining accuracy on rare subpopulations when training-group labels are unavailable. However, existing methods often infer environments from…
cs.IR2026
Conditionally Identifiable Latent-Environment Modeling for Out-of-Distribution Recommendation
Qianqian Wang, Wenwu Gong, Yunshan Li +3
Out-of-distribution (OOD) recommendation is vulnerable to preference shifts induced by a latent environment. Existing methods can infer latent states from logged interactions, yet…