@inproceedings{kook-etal-2025-empowering,
    title = "Empowering Retrieval-based Conversational Recommendation with Contrasting User Preferences",
    author = "Kook, Heejin  and
      Kim, Junyoung  and
      Park, Seongmin  and
      Lee, Jongwuk",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.naacl-long.392/",
    pages = "7692--7707",
    ISBN = "979-8-89176-189-6",
    abstract = "Conversational recommender systems (CRSs) are designed to suggest the target item that the user is likely to prefer through multi-turn conversations. Recent studies stress that capturing sentiments in user conversations improves recommendation accuracy. However, they employ a single user representation, which may fail to distinguish between contrasting user intentions, such as likes and dislikes, potentially leading to suboptimal performance. To this end, we propose a novel conversational recommender model, called COntrasting user pReference expAnsion and Learning (CORAL). Firstly, CORAL extracts the user{'}s hidden pref- erences through contrasting preference expansion using the reasoning capacity of the LLMs. Based on the potential preference, CORAL explicitly differentiates the contrasting preferences and leverages them into the recommendation process via preference-aware learning. Extensive experiments show that CORAL significantly outperforms existing methods in three benchmark datasets, improving up to 99.72{\%} in Recall@10. The code and datasets are available at https://github.com/kookeej/CORAL."
}