In Query-driven Travel Recommender Systems (RSs), it is crucial to understand the user intent behind challenging natural language(NL) destination queries such as the broadly worded "youth-friendly activities" or the indirect description "a high school graduation trip". Such queries are challenging due to the wide scope and subtlety of potential user intents that confound the ability of retrieval methods to infer relevant destinations from available textual descriptions such as WikiVoyage. While query reformulation (QR) has proven effective in enhancing retrieval by addressing user intent, existing QR methods tend to focus only on expanding the range of potentially matching query subtopics (breadth) or elaborating on the potential meaning of a query (depth), but not both. In this paper, we introduce Elaborative Subtopic Query Reformulation (EQR), a large language model-based QR method that combines both breadth and depth by generating potential query subtopics with information-rich elaborations. We also release TravelDest, a novel dataset for query-driven travel destination RSs. Experiments on TravelDest show that EQR achieves significant improvements in recall and precision over existing state-of-the-art QR methods.

翻译：在查询驱动的旅行推荐系统中，理解具有挑战性的自然语言目的地查询背后的用户意图至关重要，例如宽泛表述的“适合年轻人的活动”或间接描述的“高中毕业旅行”。此类查询之所以具有挑战性，是因为潜在用户意图范围广泛且含义微妙，这干扰了检索方法从现有文本描述（如WikiVoyage）中推断相关目的地的能力。虽然查询重构已被证明能通过处理用户意图有效增强检索效果，但现有的查询重构方法往往仅侧重于扩展潜在匹配查询子主题的范围（广度），或仅阐述查询的潜在含义（深度），而非两者兼顾。本文提出精细化子主题查询重构，这是一种基于大语言模型的查询重构方法，通过生成具有信息丰富阐述的潜在查询子主题，将广度与深度相结合。我们还发布了TravelDest，一个用于查询驱动旅行目的地推荐系统的新型数据集。在TravelDest上的实验表明，与现有最先进的查询重构方法相比，精细化子主题查询重构在召回率和精确率上均取得了显著提升。