Improving LLM-based Recommendation with Self-Hard Negatives from Intermediate Layers

• URL: http://arxiv.org/abs/2602.17410v1
• Date: Thu, 19 Feb 2026 14:37:43 GMT
• Title: Improving LLM-based Recommendation with Self-Hard Negatives from Intermediate Layers
• Authors: Bingqian Li, Bowen Zheng, Xiaolei Wang, Long Zhang, Jinpeng Wang, Sheng Chen, Wayne Xin Zhao, Ji-rong Wen,
• Abstract summary: ILRec is a novel preference fine-tuning framework for LLM-based recommender systems.<n>We introduce a lightweight collaborative filtering model to assign token-level rewards for negative signals.<n>Experiments on three datasets demonstrate ILRec's effectiveness in enhancing the performance of LLM-based recommender systems.
• Score: 80.55429742713623
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) have shown great promise in recommender systems, where supervised fine-tuning (SFT) is commonly used for adaptation. Subsequent studies further introduce preference learning to incorporate negative samples into the training process. However, existing methods rely on sequence-level, offline-generated negatives, making them less discriminative and informative when adapting LLMs to recommendation tasks with large negative item spaces. To address these challenges, we propose ILRec, a novel preference fine-tuning framework for LLM-based recommendation, leveraging self-hard negative signals extracted from intermediate layers to improve preference learning. Specifically, we identify self-hard negative tokens from intermediate layers as fine-grained negative supervision that dynamically reflects the model's preference learning process. To effectively integrate these signals into training, we design a two-stage framework comprising cross-layer preference optimization and cross-layer preference distillation, enabling the model to jointly discriminate informative negatives and enhance the quality of negative signals from intermediate layers. In addition, we introduce a lightweight collaborative filtering model to assign token-level rewards for negative signals, mitigating the risk of over-penalizing false negatives. Extensive experiments on three datasets demonstrate ILRec's effectiveness in enhancing the performance of LLM-based recommender systems.

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