AI Ranking Factors for Restaurants – 2026 Research

How AI Decides Which Restaurants to Recommend

When someone asks ChatGPT for the best Italian restaurant in Chicago, it doesn’t guess. It pulls from structured data sources, weighs the evidence, and makes a recommendation. The same is true for Google’s AI Overviews, Perplexity, and Gemini.

The question restaurant owners should be asking: what makes AI recommend one restaurant over another?

We tested this. We ran 80 queries across four AI platforms in five US cities, analyzed the Google Business Profiles of every restaurant that got recommended, and compared them to highly-rated restaurants that didn’t make the cut.

The findings surprised us. And they have significant implications for how restaurants should think about their online presence. It shows us what works as AI ranking factors for restaurants

The Research

We asked ChatGPT, Google Gemini, Perplexity, and Google AI Overviews the same questions across Chicago, Austin, Miami, Seattle, and Denver:

• “Best Italian restaurant in [city]”
• “Best Mexican restaurant in [city]”
• “Best brunch spot in [city]”
• “Romantic restaurant in [city]”

That’s 80 queries total, with each platform returning its top three recommendations.

We then analyzed the Google Business Profiles of every recommended restaurant (182 unique restaurants after removing duplicates) and compared them to a control group of 48 highly-rated restaurants that weren’t recommended by any AI platform.

The Headline Finding: It’s Not About Quality

Here’s what the data showed:

AI-Recommended Restaurants (182 restaurants)

• Average Rating: 4.66 stars
• Average Review Count: 3,424

Control Group (48 restaurants)

• Average Rating: 4.63 stars
• Average Review Count: 955

The gap: 3.6x more reviews

The rating difference is just 0.03 stars. Both groups are excellent restaurants with ratings above 4.6 stars.

The review count gap tells the real story.

AI-recommended restaurants have, on average, 3.6 times more reviews than comparable restaurants that didn’t get recommended. And “comparable” is the key word here. We’re not comparing great restaurants to mediocre ones. We’re comparing great restaurants to other great restaurants.

The difference isn’t quality. It’s volume.

The Restaurants AI Ignores

Our control group includes restaurants that would make any local’s top-10 list:

Barley Swine (Austin) – 4.7 stars, 908 reviews. From a James Beard Award-winning chef. One of Austin’s most celebrated restaurants. Not recommended by any AI platform.

Cafe Juanita (Seattle) – 4.7 stars, 797 reviews. Multiple James Beard nominations for Best Chef Northwest. Northern Italian cuisine that’s drawn acclaim for over two decades. Not recommended.

Mizuna (Denver) – 4.6 stars, 652 reviews. Denver’s premier fine dining destination. James Beard-nominated. Consistently ranked among the city’s best. Not recommended.

Provare (Chicago) – 4.8 stars, 456 reviews. Higher rated than most AI recommendations. Authentic Italian in a city full of Italian options. Not recommended.

XOchimilco (Chicago) – 4.8 stars, 514 reviews. A Chicago institution since 1993. Three decades of serving authentic Mexican food, higher rated than most AI picks. Not recommended.

These aren’t hidden holes-in-the-wall. They’re acclaimed restaurants with strong ratings and hundreds of satisfied customers. But when someone asks AI for a recommendation, they don’t come up.

Meanwhile, restaurants with 3,000-5,000 reviews get recommended consistently, even when their ratings are lower.

Why Review Volume Matters to AI

AI systems are designed to be helpful. When someone asks for a restaurant recommendation, the AI wants to give a confident, accurate answer.

Confidence requires evidence. And reviews are evidence.

A restaurant with 3,000 reviews has been validated by 3,000 separate data points. Each review contains signals: the food quality, service, ambiance, specific dishes, price perception, and overall experience. AI can parse these patterns and synthesize them into a recommendation.

A restaurant with 500 reviews, even excellent ones, offers less evidence. Less data to work with. Less confidence. Less reason to recommend.

This isn’t a quality judgment. It’s a data availability problem. AI recommends what it can verify through volume, not what’s necessarily best.

What AI Actually Reads in Your Google Business Profile

Your Google Business Profile is the primary structured data source AI systems use to understand your restaurant. Here’s what they’re parsing:

Basic Information

• Business name and category
• Location and hours
• Price level
• Contact information

Review Data

• Total review count
• Average star rating
• Review recency (are people still reviewing?)
• Review content and sentiment patterns
• Common themes across reviews

Attributes

• Dining options (dine-in, takeout, delivery)
• Amenities (outdoor seating, Wi-Fi, parking)
• Accessibility features
• Dietary options (vegetarian, vegan, gluten-free)
• Atmosphere descriptors (romantic, good for groups, family-friendly)

Additional Signals

• Photos (quantity and categorization)
• Menu items (if structured in Google)
• Q&A content
• Google Posts and updates
• Owner responses to reviews

Each of these fields gives AI more context to work with. More context means more accurate matching to user queries.

When someone searches for a “romantic Italian restaurant with outdoor seating,” the AI isn’t guessing. It’s cross-referencing your attributes, review content, and profile data to determine if you match.

Platform Differences: What We Observed

Not all AI platforms recommend the same restaurants. Across our 80 queries, there was significant variation in which restaurants each platform surfaced.

ChatGPT tended toward well-known establishments with strong brand recognition and significant media coverage. Its recommendations often included restaurants that had been featured in major publications or had celebrity chef associations.

Google Gemini showed the tightest alignment with Google Maps results, which makes sense given they share underlying data infrastructure. Gemini recommendations often matched what you’d see in a traditional Google search.

Perplexity surfaced a mix of established favorites and newer, highly-rated spots. It seemed to weight recency more heavily, recommending restaurants with recent positive momentum.

Google AI Overviews pulled from Google’s knowledge graph but often included different results than Gemini, particularly for more subjective queries like “romantic restaurant.”

What this means for restaurants: being visible to AI isn’t about optimizing for a single platform. Your Google Business Profile feeds all of them. But the way each platform weighs and interprets that data varies.

The Compounding Effect

Here’s where it gets concerning for restaurants without massive review counts.

AI recommendations drive discovery. Discovery drives visits. Visits drive reviews. Reviews feed back into AI confidence.

Restaurants that are already being recommended enter a virtuous cycle. Each recommendation brings new customers, who leave new reviews, which strengthens the AI’s confidence in recommending them again.

Restaurants that aren’t being recommended miss out on this cycle entirely. Their review count grows slowly. Their relative position weakens. The gap widens.

This isn’t happening in the future. It’s happening now.

Specific Examples from the Recommended List

Monteverde (Chicago) was recommended by three of four AI platforms for Italian food. Their profile shows 3,237 reviews at 4.8 stars, with detailed attributes covering outdoor seating, reservations, and dietary options. Their menu is structured within Google. Reviews are recent, with consistent positive sentiment around handmade pasta.

The Pink Door (Seattle) appeared in recommendations across multiple query types (Italian, romantic, brunch) on multiple platforms. They have 1,835 reviews at 4.6 stars, but what stands out is their unique positioning. Review content repeatedly mentions “cabaret,” “aerial performances,” and “hidden gem” – giving AI clear differentiating signals.

Canlis (Seattle) was recommended by every platform for romantic dining. At 5,213 reviews and a 4.4 star rating, they have the volume and the reputation. Their profile is exhaustive: full menu, complete attributes, active owner responses, recent Google Posts.

Note that Canlis has a lower rating (4.4) than Cafe Juanita (4.7), which wasn’t recommended. The difference? Canlis has 5,213 reviews versus 797. Volume wins.

What This Means for Multi-Location Restaurant Groups

If you operate multiple locations, consistency becomes critical.

Each location has its own Google Business Profile. Each location builds its own review history. Each location either gets recommended by AI or it doesn’t.

A restaurant group with 10 locations might have 3 that show up in AI recommendations and 7 that don’t. The difference often comes down to profile completeness and review volume at each specific location.

The questions to ask:

• Which locations have crossed the ~2,000 review threshold?
• Which locations are generating consistent new reviews?
• Are owner responses happening across all locations or just some?
• Is there a process for keeping each profile current?

The Practical Implications

Based on our research, here’s what correlates with being recommended by AI:

High-impact factors:

• Review volume (the 3.6x gap with quality held constant)
• Review recency (recent reviews signal ongoing relevance)
• Star rating above 4.4 (necessary but not sufficient)
• Profile completeness (every attribute filled)

Supporting factors:

• Structured menu data in Google
• Active Q&A section
• Owner responses to reviews
• Recent Google Posts
• Photo quantity and variety

What doesn’t seem to matter as much:

• Being the highest-rated option (4.8 stars doesn’t beat 4.5 stars with more reviews)
• Website design or content
• Social media following
• Third-party review platforms (Yelp, TripAdvisor matter less than Google)

The implication is clear: your Google Business Profile is how AI understands your restaurant. And right now, AI is deciding whether to recommend you based primarily on how much evidence it has, not how good that evidence is.

A Note on What We Don’t Know

This research has limitations.

We tested 80 queries across five cities and four platforms. The restaurant industry is vast. Our sample, while revealing, doesn’t capture every scenario.

We focused on Google Business Profile data because it’s the most accessible and consistent data source. AI systems also pull from news coverage, food blogs, social media mentions, and other signals we didn’t measure.

We don’t know the exact algorithms each platform uses. We can observe correlations, but we can’t prove causation. The 3.6x review gap is real, but we can’t say definitively that more reviews cause recommendations.

What we can say: highly-rated restaurants with fewer reviews consistently don’t get recommended, while similarly-rated restaurants with more reviews do. The pattern is strong enough to take seriously.

What Comes Next

AI-powered discovery is still early. ChatGPT launched search capabilities recently. Google AI Overviews are rolling out progressively. Perplexity is growing rapidly but still niche compared to traditional search.

But the direction is clear. More people will ask AI for recommendations. AI will need evidence to generate those recommendations. Review volume is currently the strongest signal of that evidence.

The restaurants that build review-rich profiles now are positioning themselves for a shift that’s already underway.

The ones that aren’t may find themselves invisible to a growing segment of potential customers, regardless of how good the food is.

Frequently Asked Questions

Download the Data

We’re making our full dataset available for anyone who wants to verify our findings or conduct their own analysis.

• Research Data (ZIP) – Complete dataset including all queries, recommended restaurants, and control group

Methodology

Query collection: 80 total queries (4 platforms × 5 cities × 4 query types). Each query recorded the top 3 recommendations with notes on AI reasoning.

Recommended restaurants: 182 unique restaurants analyzed for Google Business Profile data including rating and review count.

Control group: 48 highly-rated restaurants (4.63 average stars, range 4.4-4.8) with 200-2,000 reviews that appeared in no AI recommendations. Control restaurants were selected across the same five cities and four cuisine categories, then verified to have no overlap with the recommended list.

Platforms tested: ChatGPT (with browsing enabled), Google Gemini, Perplexity, Google AI Overviews.

Cities tested: Chicago, Austin, Miami, Seattle, Denver.

Query types: Best Italian restaurant, best Mexican restaurant, best brunch spot, romantic restaurant.

Data collected: January 2026.

This research was conducted by MyPlace, a guest engagement platform for hospitality businesses.