用大模型理解模糊最优查询，生成推荐属性并提升推荐效果
Generative Product Recommendations for Implicit Superlative Queries
- 基于LLM构建四维标注框架，自动补全隐含最优需求的属性
- 在新数据集上验证方法，显著优于传统检索排序策略
- 适合电商场景中处理模糊高阶查询，如‘最适合徒步的鞋’
在推荐系统中，用户常通过间接、模糊或不完整的方式表达对最优产品的需求，例如‘最适合徒步的鞋’。这类查询被称为隐式最高级查询，对标准检索与排序系统构成挑战，因其未明确提及属性，需识别并推理复杂因素。本文研究如何利用大语言模型（LLM）生成用于排序的隐含属性，并基于这些属性进行推理，以改进此类查询下的产品推荐。作为第一步，我们提出一种全新的四点标注方案SUPERB，并结合基于LLM的产品属性标注。随后，我们在新数据集上实证评估多种现有检索与排序方法，提供深入见解，并讨论其在真实电商生产系统中的集成可行性。
原文摘要 · Abstract (English)
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.
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