mlsanityData to 14 Oct 2026

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

arXiv 2410.01598 ↗First posted 2 Oct 2024cs.IR cs.AIcited by 11

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Abstract (arXiv)

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.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 23 of its 47 references that are in the map. First posted on arXiv 2 Oct 2024.

Builds on

23 of 47 references.

References · 23

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11 more
  1. 2023Leveraging Large Language Models in Conversational Recommender Systemscited by 63
  2. 2024A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)cited by 58
  3. 2023Query Expansion by Prompting Large Language Modelscited by 57
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 8.

11 papers.

  1. 2025SynthTRIPs: A Knowledge-Grounded Framework for Benchmark Query Generation for Personalized Tourism Recommenderscited by 8
  2. 2025ChessQA: Evaluating Large Language Models for Chess Understandingcited by 8
  3. 2026Semantic XPath: Structured Agentic Memory Access for Conversational AIcited by 7
  4. 2026Bayesian Active Learning with Gaussian Processes Guided by LLM Relevance Scoring for Dense Passage Retrievalcited by 6
  5. 2026Evaluating Scene-based In-Situ Item Labeling for Immersive Conversational Recommendationcited by 5
  6. 2026SafeGEO: Understanding Generative Engine Optimization Risks in Recommendation Agentscited by 4
  7. 2026Temporal Order Matters for Agentic Memory: Segment Trees for Long-Horizon Agentscited by 3
  8. 2026ReFormeR: Learning and Applying Explicit Query Reformulation Patternscited by 1
  9. 2026MicroVerse: An Instrument for Measuring Self-Authored Identity Drift in Long-Horizon Multi-Agent Language-Model Simulationscited by 1
  10. 2025Q-STRUM Debate: Query-Driven Contrastive Summarization for Recommendation Comparisoncited by 0
  11. 2026BoundaryMORPH: Budgeted Reranking via Active Set Selection for Diffuse Retrievalcited by 0