Estimating Probabilities in Recommendation Systems

Summary Recommendation systems are emerging as an important business application with significant economic impact. Currently popular systems include Amazon's book recommendations, Netflix's movie recommendations and Pandora's music recommendations. We address the problem of estimating probabilities associated with recommendation system data by using non-parametric kernel smoothing. In our estimation we interpret missing items as randomly censored observations of preference relations and obtain efficient computation schemes by using combinatorial properties of generating functions. We demonstrate our approach with several case-studies involving real world movie recommendation data. The results are comparable with state of the art techniques while also providing probabilistic preference estimates outside the scope of traditional recommender systems.