CURE:Circuit-Aware Unlearning for LLM-based Recommendation
Recent advances in large language models (LLMs) have opened new opportunities for recommender systems by enabling rich semantic understanding and reasoning about user interests and item attributes. However, as privacy regulations tighten, incorporating user data into LLM-based recommendation (LLMRec) introduces significant privacy risks, making unlearning algorithms increasingly crucial for practical deployment. Despite growing interest in LLMRec unlearning, most existing approaches formulate unlearning as a weighted combination of forgetting and retaining objectives while updating model parameters in a uniform manner. Such formulations inevitably induce gradient conflicts between the two...
| Authors | Ziheng Chen, Jiali Cheng, Zezhong Fan et al. |
|---|---|
| Primary category | cs.IR |
| Published | 4 April 2026 |
| Last updated at source | 4 April 2026 |
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