LLMs Leverage Self-Hard Negatives For Recommendation
Researchers (v1 submitted Feb 19, 2026) propose ILRec, a preference fine-tuning framework that improves LLM-based recommender systems by extracting self-hard negative tokens from intermediate layers. The two-stage method uses cross-layer preference optimization and distillation, plus a lightweight collaborative-filtering module to assign token-level rewards and reduce false-negative penalties. Experiments on three datasets show ILRec enhances recommendation performance versus sequence-level offline negatives.
Scoring Rationale
Novel cross-layer negative mining increases practical relevance, but arXiv preprint lacks peer review and broader benchmarks.
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