@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://aclanthology.org/2025.naacl-srw.8/",
doi = "10.18653/v1/2025.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 ``best shoes for trail running.'' 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."
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<abstract>In recommender systems, users often seek the best products through indirect, vague, or under-specified queries such as “best shoes for trail running.” 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.</abstract>
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%0 Conference Proceedings
%T Generative Product Recommendations for Implicit Superlative Queries
%A Dhole, Kaustubh
%A Vedula, Nikhita
%A Kuzi, Saar
%A Castellucci, Giuseppe
%A Agichtein, Eugene
%A Malmasi, Shervin
%Y Ebrahimi, Abteen
%Y Haider, Samar
%Y Liu, Emmy
%Y Haider, Sammar
%Y Leonor Pacheco, Maria
%Y Wein, Shira
%S 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)
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, USA
%@ 979-8-89176-192-6
%F dhole-etal-2025-generative
%X In recommender systems, users often seek the best products through indirect, vague, or under-specified queries such as “best shoes for trail running.” 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.
%R 10.18653/v1/2025.naacl-srw.8
%U https://aclanthology.org/2025.naacl-srw.8/
%U https://doi.org/10.18653/v1/2025.naacl-srw.8
%P 77-91
Markdown (Informal)
[Generative Product Recommendations for Implicit Superlative Queries](https://aclanthology.org/2025.naacl-srw.8/) (Dhole et al., NAACL 2025)
ACL
- Kaustubh Dhole, Nikhita Vedula, Saar Kuzi, Giuseppe Castellucci, Eugene Agichtein, and Shervin Malmasi. 2025. Generative Product Recommendations for Implicit Superlative Queries. In 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), pages 77–91, Albuquerque, USA. Association for Computational Linguistics.