Towards Comprehensible Recommendation with Large Language Model Fine-tuning

• URL: http://arxiv.org/abs/2508.07595v1
• Date: Mon, 11 Aug 2025 03:55:31 GMT
• Title: Towards Comprehensible Recommendation with Large Language Model Fine-tuning
• Authors: Yunze Luo, Yinjie Jiang, Gaode Chen, Xinghua Zhang, Jun Zhang, Jian Liang, Kaigui Bian,
• Abstract summary: We propose a novel Content Understanding from a Collaborative Perspective framework (CURec) for recommendation systems.<n>Curec generates collaborative-aligned content features for more comprehensive recommendations.<n>Experiments on public benchmarks demonstrate the superiority of CURec over existing methods.
• Score: 41.218487308635126
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recommender systems have become increasingly ubiquitous in daily life. While traditional recommendation approaches primarily rely on ID-based representations or item-side content features, they often fall short in capturing the underlying semantics aligned with user preferences (e.g., recommendation reasons for items), leading to a semantic-collaborative gap. Recently emerged LLM-based feature extraction approaches also face a key challenge: how to ensure that LLMs possess recommendation-aligned reasoning capabilities and can generate accurate, personalized reasons to mitigate the semantic-collaborative gap. To address these issues, we propose a novel Content Understanding from a Collaborative Perspective framework (CURec), which generates collaborative-aligned content features for more comprehensive recommendations. \method first aligns the LLM with recommendation objectives through pretraining, equipping it with instruction-following and chain-of-thought reasoning capabilities. Next, we design a reward model inspired by traditional recommendation architectures to evaluate the quality of the recommendation reasons generated by the LLM. Finally, using the reward signals, CURec fine-tunes the LLM through RL and corrects the generated reasons to ensure their accuracy. The corrected reasons are then integrated into a downstream recommender model to enhance comprehensibility and recommendation performance. Extensive experiments on public benchmarks demonstrate the superiority of CURec over existing methods.

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