Generative Product Recommendations for Implicit Superlative Queries
This addresses a problem in e-commerce recommender systems for users making under-specified queries, but it is incremental as it builds on existing LLM and ranking methods.
The paper tackled the challenge of recommending products for vague queries like 'best shoes for trail running' by using Large Language Models to generate and reason over implicit attributes, and introduced a new annotation schema and dataset called SUPERB for evaluation.
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.