[tuscarok]

How to determine what restaurants a person will like based on their past ratings. This is not trivial; it is difficult to say what are the features which determine why a person likes a restaurant, so I'm thinking this would be a problem that could be tackled with deep learning i.e., automatic learning of features rather than hand-crafting in advance.

This is similar to the problem of movie/book recommendations, which has not been solved to my satisfaction. I have yet to see any good recommendation systems out there which go beyond recommending movies based on simple things like 'same actor was in this movie', 'people who watched that watched this' etc.

[gyre007]

The problem with recommendations in general is that they are subjective to people's feelings, often times at a particular point of time.

This is applicable to movies and restaurants. Sometimes I'm in a mood for an action movie but that doesn't mean I want to be seeing those all the time around. Similarly, there is simply time when I'm in a mood for mexican or Indian and not Italian where I may have been going to for past month.

Unless we manage to capture the inner motivations/feelings (maybe based on our online behavioural patterns?) our recommendations will just be artificially built mashed potatoes crap.

[curiously]

that's a great point, however the fact that those haven't been solved yet suggests that the problem is actually very difficult. These type of problems I would shy away from because the results wouldn't be consistent.

For example, catching a fraudster based on their actions for your website is easy because you can create models with scope limited to your own website with few constraints vs. catching any fraud on any website because they all have different processes, scopes and constraints wildly fluctuate.