Reviews as training data
AI systems learn about hospitality properties partly from review content. When an AI reads thousands of TripAdvisor reviews, it extracts patterns: which properties are consistently described with which attributes, in which context, by guests who match which profile.
A hotel where the most common review terms are “convenient location”, “standard rooms”, “nothing special” is taught to the AI as a generic, undifferentiated property. A hotel where reviews consistently mention “the only hotel in the city with a working fireplace in every room”, “best espresso bar attached to a hotel I’ve ever had”, or “within sight of the cathedral from the breakfast table” is taught to the AI as a distinctive, attribute-rich property that matches specific queries.
Which review platforms the AI reads
All of them, weighted differently:
TripAdvisor is the most influential review source for AI travel recommendations. Its scale, longevity and structured data (photos, room tags, traveller type) make it a high-weight source. Perplexity in particular cites TripAdvisor frequently.
Google Reviews feed directly into Google AI Overview. This is separate from TripAdvisor and requires attention as a distinct channel.
Booking.com and Airbnb reviews are read by AI systems that crawl those platforms. The review content on these platforms contributes to the AI’s entity profile for your property.
Expedia and Hotels.com reviews contribute when AI systems crawl those platforms, though their weight is lower than TripAdvisor and Google.
The three qualities that matter most
Volume. More reviews means more training signal. A property with 500 reviews is better represented in AI training data than a property with 50, even if both have the same average rating.
Recency. AI systems give more weight to recent content. A property that was excellent in 2019 and mediocre in 2023 may have a high overall rating but a declining AI recommendation profile, because recent reviews are dominating the AI’s current understanding.
Attribute specificity. Generic reviews (“great hotel, would recommend”) provide minimal AI signal. Specific reviews (“the private rooftop jacuzzi is only accessible to guests in the penthouse suite, and it was absolutely worth the premium”) provide rich attribute data that helps the AI match your property to specific queries.
What you can do about your review profile
Respond to every review. Review responses are also read by AI systems. Responses that confirm or add specificity to what a guest mentioned (“We’re so glad you enjoyed the terrace overlooking the Bosphorus. It’s our guests’ favourite feature, especially at sunset”) reinforce those attribute associations.
Ask guests for specific mentions. In post-stay communications, it is legitimate (and common practice) to ask guests to mention a specific aspect of their stay in their review if they’d like to help others. Frame it as a specific request: “If you have a moment, it would mean a lot to read what you thought of the breakfast service” rather than a generic “please leave us a review”.
Address the gaps. If your property has attributes that guests often don’t mention (a quiet garden, a particularly helpful concierge, a distinctive architectural feature), find natural ways to prompt those mentions. Signage that says “Our garden is award-winning: 47% of guests say it’s their favourite part of the property” gives departing guests a specific attribute to consider including.
See also: What is entity salience for hotels | Get found: the technical playbook