The task I'm used to do is the following. A client comes to see me with a set of clients (called positive companies) and he wants me to find other similar prospects. Usually, he also gives me a set of negatives companies and I have a big set of potential companies (that I call the basket).
I perform this task by doing a Adaboost classifier that I train with the positives and negatives. I then run this classifier on the basket. Each company in the basket receives a score and the highest score shows the most promising prospects for the client.
Now, a new client doesn't have any set of negatives to give and I'm a little bit lost. I can not do a supervised learning anymore, obviously. I first thought of performing a k-nearest neighbours on each positive and I would receive a list of "close" prospects. The problem with that is that I don't have a score anymore. Furthermore, with the k-nearest method, I should define a distance which I don't like because I don't want to give subjective weights to features. Indeed, the Adaboost classifier would learn some weights and would itself predict which features are important.
Could someone indicate me how I could tackle this problem?