@inproceedings{dhole-etal-2025-generative,
    title = "Generative Product Recommendations for Implicit Superlative Queries",
    author = "Dhole, Kaustubh  and
      Vedula, Nikhita  and
      Kuzi, Saar  and
      Castellucci, Giuseppe  and
      Agichtein, Eugene  and
      Malmasi, Shervin",
    editor = "Ebrahimi, Abteen  and
      Haider, Samar  and
      Liu, Emmy  and
      Haider, Sammar  and
      Leonor Pacheco, Maria  and
      Wein, Shira",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 4: Student Research Workshop)",
    month = apr,
    year = "2025",
    address = "Albuquerque, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Ingest-2025-COMPUTEL/2025.naacl-srw.8/",
    pages = "77--91",
    ISBN = "979-8-89176-192-6",
    abstract = "In recommender systems, users often seek the best products through indirect, vague, or under-specified queries such as {\textquotedblleft}best shoes for trail running.{\textquotedblright} These queries, referred to as implicit superlative queries, pose a challenge for standard retrieval and ranking systems due to their lack of explicit attribute mentions and the need for identifying and reasoning over complex attributes. We investigate how Large Language Models (LLMs) can generate implicit attributes for ranking and reason over them to improve product recommendations for such queries. As a first step, we propose a novel four-point schema, called SUPERB, for annotating the best product candidates for superlative queries, paired with LLM-based product annotations. We then empirically evaluate several existing retrieval and ranking approaches on our newly created dataset, providing insights and discussing how to integrate these findings into real-world e-commerce production systems."
}