Recommendation engines are generally built such that a single kind of user interaction with a single kind of item is used to suggest the same kind of interaction with the same kind of item. In practice however, this approach is flawed for several reasons. First, multiple kinds of interactions with multiple kinds of items are typically available and should be used. Second, recommendation is better viewed as a ranking problem rather than a regression problem. Finally, practical recommendation systems should be constantly self-training as today’s recommendations and selections can be used to train tomorrow’s recommender.
This session will describe a practical recommendation architecture and implementation style that addresses all of the above issues and which is considerably easier to implement and deploy than conventional approaches. Several of the techniques that I will describe have never (to my knowledge) appeared in the research literature.
Published on December 16th 2013