{"ID":2883524,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.07595","arxiv_id":"2508.07595","title":"Towards Comprehensible Recommendation with Large Language Model Fine-tuning","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.","short_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),...","url_abs":"https://arxiv.org/abs/2508.07595","url_pdf":"https://arxiv.org/pdf/2508.07595v1","authors":"[\"Yunze Luo\",\"Yinjie Jiang\",\"Gaode Chen\",\"Xinghua Zhang\",\"Jun Zhang\",\"Jian Liang\",\"Kaigui Bian\"]","published":"2025-08-11T03:55:31Z","proceeding":"cs.IR","tasks":"[\"cs.IR\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}