2025/02/04 by Yi Fang, Wenjie Wang, Fang, Yi +11 · 10 citations
Computer Science · #Recommender Systems and Techniques #Sentiment Analysis and Opinion Mining #Topic Modeling #cs.IR
paper · pdf · doi:10.48550/arxiv.2502.02061
openalex publication_date 2025/02/04 · openalex created_date 2025/02/06 · openalex updated_date 2026/07/31
Aligning Large Language Models (LLMs) with recommendation tasks represents an emerging paradigm in recommendation domain, exhibiting promising performance overall. However, these aligned recommendation LLMs often struggle with complex scenarios due to limitations in the current alignment task formulation, which optimizes LLMs to directly generate user feedback without deliberation. To develop more reliable recommendation LLMs, we introduce a new Deliberative Recommendation task, which enforces explicit reasoning about user preferences as an additional alignment objective. To address this task, we propose a Reasoning-powered Recommender framework designed to enhance reasoning capabilities by leveraging verbalized user feedback in a step-wise manner. Specifically, this framework employs collaborative step-wise experts alongside specifically crafted expert-wise training strategies. Extensive experiments conducted on three real-world datasets demonstrate the rationality of the deliberative task formulation and the effectiveness of the proposed framework in improving both prediction accuracy and reasoning quality. Our implementation is publicly available on GitHub: https://github.com/Peter-Fy/Reason4Rec.