Netflix is trying to motivate research in the area of recommender systems and on Oct. 2, 2006 offered $1 million to anyone that could improve upon their current recommender system by a specific measure (improve RMSE by 10%). Recently I took a look at the current standings and one team is very close (improvement around 9%). Interestingly enough they had a few papers showing how they do it.
Specifically what we are talking about is collaborative filtering. There are two main approaches, either you look for global patterns in the matrix of ratings or you use the ratings from similar items or users. BellKor (team name) was able to successfully merge these two ideas into a single solution that outperformed (at the time of submission) any other approaches using one of the two approaches.
What impressed me most about the paper I read (Factorization Meets the Neighborhood: a Multifaceted Collaborative Filtering Model) was that in addition to testing RMSE for the test, they tried to look at the users perspective. We want to know what movie to watch now. They compared other approaches against theirs on whether they would recommend in the top 5 or top 20 a movie you would watch and rate a 5. Well done. We should all keep the end user in mind.
Any one have a really good or bad experience with recommendations made by computers?
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