Elaborative Subtopic Query Reformulation for Broad and Indirect Queries in Travel Destination Recommendation

TL;DR

This paper introduces EQR, a novel large language model-based query reformulation method that enhances travel destination retrieval by combining breadth and depth in understanding user queries, supported by a new dataset.

Contribution

The paper presents EQR, a new query reformulation approach that integrates both broadening and elaborating user queries, and introduces the TravelDest dataset for travel recommendation systems.

Findings

EQR significantly improves recall and precision in destination retrieval.

EQR outperforms existing query reformulation methods.

The TravelDest dataset supports advanced research in travel recommendation.

Abstract

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.