5 Problems of Recommender Systems
Earlier this week we posted a Guide to Recommender Systems, as part of our series on recommendation technologies. In this post we look at some of the challenges in building or deploying a recommender system. And yes, Napoleon Dynamite is one of them.
This week an event called Recked was held in Amsterdam, aimed at engineers interested in these systems. The event was hosted by Wakoopa and Strands (we've embedded the presentations below). In those presentations, there were some hints at the problems that these companies have to overcome to build an effective recommender system.
Perhaps the biggest issue facing recommender systems is that they need a lot of data to effectively make recommendations. It's no coincidence that the companies most identified with having excellent recommendations are those with a lot of consumer user data: Google, Amazon, Netflix, Last.fm. As illustrated in the slide below from Strands' presentation at Recked, a good recommender system firstly needs item data (from a catalog or other form), then it must capture and analyze user data (behavioral events), and then the magic algorithm does its work. The more item and user data a recommender system has to work with, the stronger the chances of getting good recommendations. But it can be a chicken and egg problem - to get good recommendations, you need a lot of users, so you can get a lot of data for the recommendations.
