Classification gives you calibrated scores that you can use together with other information. A classifier may not be the ideal recommender by itself but it is a good component.
A probability score for “will he like it?” works as a ranking score in my experience with some caveats which aren’t so much about the score as a score but that recommendation is really a sequential problem. That is, if I get different versions of the same news article that all score 0.9, it might be OK to show me one or two articles from that list. I believe people’s satisfaction with an article is greatly influenced by being spammed with too much of the same thing and that is not so influenced by ranking scores.
I’ll go so far as to say full text search should also be treated as a classification problem, in particularly you need a probability score if you want to make a service like “Google Alerts” where you tell people about new marching documents. Also if you are trying to combine several radically different searchers (like IBM Watson did back in the day) the probability score is essential.
A probability score for “will he like it?” works as a ranking score in my experience with some caveats which aren’t so much about the score as a score but that recommendation is really a sequential problem. That is, if I get different versions of the same news article that all score 0.9, it might be OK to show me one or two articles from that list. I believe people’s satisfaction with an article is greatly influenced by being spammed with too much of the same thing and that is not so influenced by ranking scores.
I’ll go so far as to say full text search should also be treated as a classification problem, in particularly you need a probability score if you want to make a service like “Google Alerts” where you tell people about new marching documents. Also if you are trying to combine several radically different searchers (like IBM Watson did back in the day) the probability score is essential.