mlsanityData to 14 Oct 2026

Generative Product Recommendations for Implicit Superlative Queries

arXiv 2504.18748 ↗First posted 26 Apr 2025cs.IR cs.CLcited by 5

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Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 30 of its 50 references that are in the map. First posted on arXiv 26 Apr 2025.

Builds on

30 of 50 references.

References · 30

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18 more
  1. 2023Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Predictioncited by 116
  2. 2023Beyond Yes and No: Improving Zero-Shot LLM Rankers via Scoring Fine-Grained Relevance Labelscited by 86
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 3.

5 papers.

  1. 2026RubricRAG: Towards Interpretable and Reliable LLM Evaluation via Domain Knowledge Retrieval for Rubric Generationcited by 11
  2. 2025WeMusic-Agent: Efficient Conversational Music Recommendation via Knowledge Internalization and Agentic Boundary Learningcited by 2
  3. 2025Hint-Augmented Re-ranking: Efficient Product Search using LLM-Based Query Decompositioncited by 1
  4. 2026MMGRid: Navigating Temporal-aware and Cross-domain Generative Recommendation via Model Mergingcited by 1
  5. 2026From Unstructured to Structured: LLM-Guided Attribute Graphs for Entity Search and Rankingcited by 0