@inproceedings{bismay-etal-2025-reasoningrec,
    title = "{R}easoning{R}ec: Bridging Personalized Recommendations and Human-Interpretable Explanations through {LLM} Reasoning",
    author = "Bismay, Millennium  and
      Dong, Xiangjue  and
      Caverlee, James",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.findings-naacl.454/",
    doi = "10.18653/v1/2025.findings-naacl.454",
    pages = "8132--8148",
    ISBN = "979-8-89176-195-7",
    abstract = "This paper presents ReasoningRec, a reasoning-based recommendation framework that leverages Large Language Models (LLMs) to bridge the gap between recommendations and human-interpretable explanations. In contrast to conventional recommendation systems that rely on implicit user-item interactions, ReasoningRec employs LLMs to model users and items, focusing on preferences, aversions, and explanatory reasoning. The framework utilizes a larger LLM to generate synthetic explanations for user preferences, subsequently used to fine-tune a smaller LLM for enhanced recommendation accuracy and human-interpretable explanation. Our experimental study investigates the impact of reasoning and contextual information on personalized recommendations, revealing that the quality of contextual and personalized data significantly influences the LLM{'}s capacity to generate plausible explanations. Empirical evaluations demonstrate that ReasoningRec surpasses state-of-the-art methods by up to 12.5{\%} in recommendation prediction while concurrently providing human-intelligible explanations."
}