Restaurant Discovery Is Broken – Nobody Has Fixed It With AI Yet

01 /The Concept

TravelMind is an app that helps travelers find restaurants, cafés, and bars that actually match their taste – not the algorithm's taste.

The problem: the most popular tools fail at this. Google Maps pushes paid placements. TripAdvisor surfaces "highly rated" spots – rated by people whose preferences may have nothing to do with yours.

TravelMind claims to show the places that fit *your* taste specifically, ranked by how closely they match.

The training mechanism is simple: swipe venues left or right (like/dislike). The app's AI analyzes those signals and uses them to recommend venues in any city that match what the user has demonstrated they enjoy.

The AI draws on existing venue descriptions and reviews – from the venue's own site, Google Maps, TripAdvisor, and similar platforms. The real play is how it interprets that content for each specific user.

In plain terms: if a user consistently swiped right on venues described as cozy, quiet, and good for working – the app won't recommend a trendy spot with a vibrant atmosphere and high ratings, even if everyone else loves it.

The same source data produces entirely different ranked lists for different users – regardless of ratings or whether other people found reasons to praise or criticize a place. What others loved might be exactly what you'd hate, and vice versa.

The business model is venue advertising. A venue can pay to move up in results – but only if it actually matches the user's preferences, ideally served with the photo and description most likely to resonate with that user's taste. The same AI handles that optimization.

TravelMind launched this week, announced via Product Hunt.

02 /Why It Matters

TravelMind's founder came to this idea through her own experience. Arriving in an unfamiliar city, she'd spend hours scrolling through venues, filtering out influencer picks that reliably surfaced the trendiest spots rather than the most fitting ones.

Her diagnosis: the problem isn't a shortage of options – it's that recommendation engines "ignore individual taste." Everyone sees the same rankings, despite the fact that people want fundamentally different things.

To be fair, not every recommendation engine completely ignores user preferences. But it's safe to say that most of the systems people use daily are built on older architectures with older limitations.

Google News, for instance, has learned that headphones are interesting – and genuinely serves up new articles about them. But it cannot grasp that only full-size, wired, over-ear models are relevant. On-ears and wireless are noise.

Spotify's recommendation loop became genuinely annoying at some point – cycling through the same tracks repeatedly. Qobuz offered a marginally better algorithm, plus higher-resolution audio. But even Qobuz, having more or less worked out that French-language music is the preferred genre, can't figure out that only female vocals within that genre actually land.

Amazon's book recommendations surface bestselling business titles because they have high ratings – ratings driven by a mass audience that happily consumes motivational fare that has nothing to do with what a more demanding reader is after. Netflix recommends crime thrillers because the watch history contains crime thrillers – without understanding that what makes a thriller worth watching varies enormously from person to person.

And none of these systems handle context or mood. It would be nice to listen to something completely different while working out without permanently corrupting the recommendation model. But most systems nail preferences to a static user profile with no situational awareness.

Meanwhile, AI is already capable of understanding not just that someone likes French music, but that within French music they specifically want female vocals – and not the rap subgenre that happens to share some sonic characteristics.

03 /Opportunities

Surprisingly, recommendation systems across most domains are still not using AI deeply enough – or are applying it to insufficient depth of analysis to genuinely personalize to user taste.

A parallel trend: there are now quite a few startups using AI to simulate real users – letting companies test how a pitch deck, social post, video, or new product feature lands with a synthetic audience. Examples include Artificial Societies ([covered previously](/review/ljudi-ochen-predskazuemy-i-jetim-nuzhno-nauchitsja-polzovatsja)), Synthetiq, and Lakmoos.

But recommendation is the inverse challenge. The goal is to create an AI simulation of *one specific user's* taste – then use that model to score and rank products, venues, songs, or items in a way that individual user would actually prefer. The loop continues: train on real feedback, refine the model. The problem is clearly tractable. Someone just needs to get on it.

So – in which area do you feel most underserved by current recommendation systems? The market for building a genuinely taste-aware engine is wide open.