Estimating Probabilities in Recommendation Systems

Details Estimating Probabilities in Recommendation Systems Estimating Probabilities in Recommendation Systems

2010-12-02

arxiv.org/abs/1012.0498v1

Computer Science

Learning

Scientific paper

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. In this paper we address the problem of estimating probabilities associated with recommendation system data using non-parametric kernel smoothing. In our estimation we interpret missing items as randomly censored observations and obtain efficient computation schemes 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.

Affiliated with

Also associated with

No associations

Rating

Estimating Probabilities in Recommendation Systems does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Estimating Probabilities in Recommendation Systems, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Estimating Probabilities in Recommendation Systems will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-604392