Generative product recommendations for implicit superlative queries

Kaustubh Dhole, Nikhita Vedula, Saar Kuzi, Giuseppe Castellucci, Eugene Agichtein

Abstract:

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 attributes. 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.