It would honestly be so nice to just rate a bunch of restaurants (or anything, music would be really nice) and have it spit out recommendations like “people with really similar taste to yours also like x,y,z”
This was literally the rationale behind the relaunch of reviews in Google Maps in 2010-11, and what you describe was exactly the internal demo that got the project staffed and funded. I'm not sure how much of it still exists, but for a while, when searching on maps, there were little tags on recommended places that said something like "recommended because people like you also liked X".
That project never really reached its full potential for other reasons, but it turns out that even with a lot of data, it's hard to make big gains in ranking over just using the aggregate of all users. The intersection of two people's tastes is sort of surprisingly not very helpful.
It's present for me as a % match to a restaurant I'm a regular at back home. Signals I have seen in this are menu item similarity, photo count by similar reviewers, and more basic metadata.
It didn't really translate well to visiting Japan, but does great when in a new metro and not having any personal recommendations to go off.
Good to know. I remember the Tokyo team working on menu item extraction back then, hopefully they managed to make it work. I don't believe we were looking at photo count at all - probably user submitted photos weren't a feature on Maps at the time.
The cross city problem is one of the things I've looked at a lot (I actually wrote a design doc for it at a different company). Even with really good coverage in New York/San Francisco/London/Tokyo due to our teams being based there, it was hard to get significant personalized ranking improvement. Maybe it's better now that they have a lot more data.
The one area where I have had success with this approach is with music. Various services will have public playlists that random listeners have put together. If I find a list that has a number of songs I like on it, there's a good chance I'll find something new I like on it that had never been suggested by one of the algos.
Google maps does this, for me at least. If I look at a restaurant in the app, it'll give a little XX% match score based on their algorithmic impression of my taste.
