用大模型同时扩展查询广度和深度，提升复杂意图推荐效果
A Simple but Effective Elaborative Query Reformulation Approach for Natural Language Recommendation
- 结合广度与深度的查询重构方法，生成信息丰富的子主题
- 在旅行、酒店、餐厅三类新基准上显著超越现有方法
- 适合处理模糊或间接用户意图的自然语言推荐场景
自然语言推荐系统旨在从自由文本查询和物品描述中检索相关项目。现有系统多依赖密集检索（DR），难以理解表达宽泛（如“适合年轻人活动的城市”）或间接意图（如“高中毕业旅行城市”）的挑战性查询。尽管查询重构（QR）被广泛采用以改善系统表现，但现有方法通常只关注扩大查询子主题范围（广度）或深化语义理解（深度），而无法兼顾二者。本文提出EQR（Elaborative Subtopic Query Reformulation），一种基于大语言模型的QR方法，通过生成带有丰富信息的潜在子主题，同时实现广度与深度的拓展。我们还构建了三个新的自然语言推荐基准，涵盖旅行、酒店和餐厅领域，用于评估具有挑战性查询的推荐性能。实验表明，EQR在多种评估指标上显著优于当前最优的QR方法，证明了一种简单但有效的查询重构策略可显著提升对宽泛和间接用户意图的自然语言推荐效果。
原文摘要 · Abstract (English)
Natural Language (NL) recommender systems aim to retrieve relevant items from free-form user queries and item descriptions. Existing systems often rely on dense retrieval (DR), which struggles to interpret challenging queries that express broad (e.g., "cities for youth friendly activities") or indirect (e.g., "cities for a high school graduation trip") user intents. While query reformulation (QR) has been widely adopted to improve such systems, existing QR methods tend to focus only on expanding the range of query subtopics (breadth) or elaborating on the potential meaning of a query (depth), but not both. In this paper, we propose EQR (Elaborative Subtopic Query Reformulation), a large language model-based QR method that combines both breadth and depth by generating potential query subtopics with information-rich elaborations. We also introduce three new natural language recommendation benchmarks in travel, hotel, and restaurant domains to establish evaluation of NL recommendation with challenging queries. Experiments show EQR substantially outperforms state-of-the-art QR methods in various evaluation metrics, highlighting that a simple yet effective QR approach can significantly improve NL recommender systems for queries with broad and indirect user intents.
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