Causal Direct Preference Optimization for Distributionally Robust Generative Recommendation
Direct Preference Optimization (DPO) guides large language models (LLMs) to generate recommendations aligned with user historical behavior distributions by minimizing preference alignment loss. However, our systematic empirical research and theoretical analysis reveal that DPO tends to amplify spurious correlations caused by environmental confounders during the alignment process, significantly undermining the generalization capability of LLM-based generative recommendation methods in out of distribution (OOD) scenarios. To mitigate this issue, we propose CausalDPO, an extension of DPO that incorporates a causal invariance learning mechanism. This method introduces a backdoor adjustment st...
| Authors | Chu Zhao, Enneng Yang, Jianzhe Zhao et al. |
|---|---|
| Primary category | cs.IR |
| Published | 21 March 2026 |
| Last updated at source | 27 May 2026 |
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