recommendation systems and the cold start problem

Question

Hello, I am curious what are the methods / approaches to overcome the "cold start" problem where when a new user or an item enters the system, due to lack of info about this new entity, making recommendation is a problem.

I can think of doing some prediction based recommendation (like gender, nationality and so on).

Thanks

Answer 1

Maybe there are times you just shouldn't make a recommendation? "Insufficient data" should qualify as one of those times.

I just don't see how prediction recommendations based on "gender, nationality and so on" will amount to more than stereotyping.

IIRC, places such as Amazon built up their databases for a while before rolling out recommendations. It's not the kind of thing you want to get wrong; there are lots of stories out there about inappropriate recommendations based on insufficient data.

Answer 2

This has been asked several times before (naturally, I cannot find those questions now :/, but the general conclusion was it's better to avoid such recommendations. In various parts of the worls same names belong to different sexes, and so on ...