How large language models choose which hotels to suggest to travelers.
An LLM recommendation occurs when a large language model — such as ChatGPT, Claude, or Gemini — suggests a specific hotel in response to a traveler's query. These recommendations are generated by synthesizing information from the model's training data and, in many cases, real-time web search results. Unlike traditional search results, LLM recommendations are presented as curated, conversational suggestions with explanations for why each property is recommended.
LLM recommendations represent a fundamental shift in how travelers discover hotels. When an AI assistant recommends your property, it carries an implicit endorsement that travelers trust deeply. Hotels that consistently appear in LLM recommendations benefit from higher-quality leads, as these travelers have already received a personalized rationale for why your property suits their needs.
Monitor your hotel's appearance in LLM responses by regularly testing relevant travel queries across ChatGPT, Claude, Gemini, and Perplexity. Track not just whether you appear, but how accurately and positively your hotel is described. Opally automates this process across thousands of query variations to provide comprehensive recommendation tracking.
LLM recommendations carry implicit trust and drive high-quality booking intent.
AI models synthesize data from multiple sources to form recommendations.
The quality and consistency of your online presence directly influences LLM mentions.
Each AI platform weighs different data sources, requiring a multi-platform strategy.
Monitoring LLM recommendations across platforms reveals optimization opportunities.
