Generative Product Recommendations for Implicit Superlative Queries
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912347200684032 |
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| author | Dhole, Kaustubh D. Vedula, Nikhita Kuzi, Saar Castellucci, Giuseppe Agichtein, Eugene Malmasi, Shervin |
| author_facet | Dhole, Kaustubh D. Vedula, Nikhita Kuzi, Saar Castellucci, Giuseppe Agichtein, Eugene Malmasi, Shervin |
| contents | In Recommender Systems, users often seek the best products through indirect, vague, or under-specified queries, such as "best shoes for trail running". Such queries, also referred to as implicit superlative queries, pose a significant challenge for standard retrieval and ranking systems as they lack an explicit mention of attributes and require identifying and reasoning over complex factors. We investigate how Large Language Models (LLMs) can generate implicit attributes for ranking as well as reason over them to improve product recommendations for such queries. As a first step, we propose a novel four-point schema for annotating the best product candidates for superlative queries called SUPERB, paired with LLM-based product annotations. We then empirically evaluate several existing retrieval and ranking approaches on our new dataset, providing insights and discussing their integration into real-world e-commerce production systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_18748 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Generative Product Recommendations for Implicit Superlative Queries Dhole, Kaustubh D. Vedula, Nikhita Kuzi, Saar Castellucci, Giuseppe Agichtein, Eugene Malmasi, Shervin Information Retrieval Computation and Language In Recommender Systems, users often seek the best products through indirect, vague, or under-specified queries, such as "best shoes for trail running". Such queries, also referred to as implicit superlative queries, pose a significant challenge for standard retrieval and ranking systems as they lack an explicit mention of attributes and require identifying and reasoning over complex factors. We investigate how Large Language Models (LLMs) can generate implicit attributes for ranking as well as reason over them to improve product recommendations for such queries. As a first step, we propose a novel four-point schema for annotating the best product candidates for superlative queries called SUPERB, paired with LLM-based product annotations. We then empirically evaluate several existing retrieval and ranking approaches on our new dataset, providing insights and discussing their integration into real-world e-commerce production systems. |
| title | Generative Product Recommendations for Implicit Superlative Queries |
| topic | Information Retrieval Computation and Language |
| url | https://arxiv.org/abs/2504.18748 |