Mapstr Matching: your personalized suggestions

The revolution in address discovery, with recommendations that match you 100%!

Artificial intelligence to match your tastes

Your partner in discovering your future favorites

With over 4 million users in 90 countries, mapstr is already your indispensable ally for saving and sharing your favorite addresses. Now we're taking it a step further with Mapstr Matching, a revolutionary technology that transforms the way you discover new addresses.

Mapstr matching illustration 2

Never run out of addresses again

Mapstr Matching is like having a friend in every city who knows your tastes. Wherever you are in the world, we'll suggest addresses that suit you best.

Discover your new addresses

100% personalized suggestions for you

Mapstr Matching is based on your saved favorite places and, thanks to Machine Learning, compares your desires, interests and habits with those of our global community. This unique process gives you access to tailor-made recommendations, perfectly aligned with your personal tastes.

Discover your suggestions

No more last-minute Google searches

Forget the hours spent searching for the perfect location. Mapstr Matching offers you an organized selection of places to discover and organize with your favorite tags, perfect for spontaneous or carefully planned adventures. Save time for what really matters: new experiences!

Save time

What our users say

Rated 4.8/5 on the App Store
Émilie
"Perfect when you're traveling! I scout out bars and restaurants (and even places to visit) before I leave and once I'm there I have everything at my fingertips, it's great!"
Charles
"The app that's been missing for keeping track of good addresses, browsing them visually and sharing Map with friends and family!"
Christophe
"My epicurean notepad, the app helps me keep my favorite places (restaurants, spots, hotels, ect.) in one place."
Maeva
"I love this app, being able to follow users and cards already created gives us the opportunity to discover new nuggets 👌"
David
"Great app when you're traveling and don't want to carry around the Guide du Routard like a tourist!"
Anissa
"An avid user of this app, I can't do without it! Especially in the jungle that is Paris in terms of food offerings 🤭"
Matthieu
"Indispensable! I've saved my best addresses on it and when I'm wandering around Paris or other cities, I get out mapstr to orient myself and find them again!"
Eva
"Mapstr is myPlace notebook whenever I go on a trip. An app that helps in life, you always remember the good places. Great for giving advice to friends!"
Quentin
"Really handy for entering our best addresses to try out or those we've already tested. The customizable tags are very practical and the addresses added to a map are really great! "
Clotilde
"Top app, I use it daily to prepare my outings 😊 The interface is intuitive and the app is fluid and works worldwide!!!"

💛
Mapstr supports you in your daily life

+ Over 93 M

Addresses already present

We reference places all over the world!

+ More than 4 M

Passionate users

Benefit from personalized recommendations and contribute to an enriching collaborative experience.

+ 1000

Quality partners

Get inspiration from the experts!

Our other features

No more notes at the bottom of your phone that you can never find again, no more screenshots and no more posts saved on Instagram.

Sort your addresses easily to organize your Map according to your needs.
I discover
Organize your travels and be alerted when you pass by your favorite places
I discover
Find your friends' favourite addresses by adding them on mapstr
I discover

Mapstr still have secrets for you?

Where can I find Mapstr Matching in my application?

First make sure your Mapstr application is up to date, or update it on the Apple store.

This new feature can then be accessed from your Map via the purple "Magic Wand" button in the top right-hand corner, or from the first section of the "Discover" tab.

How does Mapstr Matching work?

Mapstr Matching draws on the strength of the mapstr community and the intelligence of Machine Learning technology. This feature is based on the places a user has added to their map, and compares their desires, interests and habits with all the other users in the world to give them access to tailor-made recommendations of places, adapted to their personal tastes.

It's like having a friend in every city, who knows exactly what you like and recommends the best spots!

Why is the Mapstr Matching algorithm unique?

You've probably already seen the results of Netflix or Spotify's algorithms for recommending similar films or music.

Generally speaking, these platforms use "collaborative filtering" and often even "implicit collaborative filtering" algorithms to compare users with each other based on the films and music they've watched (without an unwatched film being perceived as a negative rating, hence the implicit).

There are lots of papers and tools explaining how to do collaborative filtering, lots of off-the-shelf solutions that could be used. Except that Netflix has around 5,000 films in its catalog... Spotify has around 60 million, with lots of analyzable criteria, such as music style, tempo, band, musicians, etc. On these platforms, the "hits" are the most important. On these platforms, the "hits" are watched or listened to by a large proportion of their users.

On the other hand, mapstr already contains more than 90 million registered places, with no factual criteria that can be organized ("French restaurant" is vague...), among which the most popular are only "added" by less than 2% of users! In terms of matrix calculation, we end up with matrices filled with 0s, and some very rare 1s, in proportions that have nothing to do with those of Netflix & co... and therefore wouldn't work with all the "classic" algorithms.

This is why Mapstr Matching is unique! It's a considerable amount of work carried out by the mapstr team, based on unique data: the mix of our users' visit intentions and approved visits.

Mapstr Is Matching available on Andoid?

Mapstr Matching is not yet available on Android, but will be soon - stay tuned!