Netflix Prize

Known as: Commendo 
The Netflix Prize was an open competition for the best collaborative filtering algorithm to predict user ratings for films, based on previous ratings… (More)
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Topic mentions per year

Topic mentions per year

2006-2016
0102020062016

Papers overview

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Highly Cited
2009
Highly Cited
2009
We consider the problem of producing recommendations from collective user behavior while simultaneously providing guarantees of… (More)
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Highly Cited
2008
Highly Cited
2008
Many recommendation systems suggest items to users by utilizing the techniques of collaborative filtering (CF) based on… (More)
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Highly Cited
2008
Highly Cited
2008
Our RMSE=0.8643 2 solution is a linear blend of over 100 results. Some of them are new to this year, whereas many others belong… (More)
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Highly Cited
2008
Highly Cited
2008
Collaborative filtering (CF) approaches proved to be effective for recommender systems in predicting user preferences in item… (More)
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2008
2008
The team “BellKor in BigChaos” is a combined team of team BellKor and BigChaos. The solution with a RMSE of 0.8616 is created by… (More)
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Highly Cited
2007
Highly Cited
2007
This article outlines the overall strategy and summarizes a few key innovations of the team that won the first Netflix progress… (More)
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Highly Cited
2007
Highly Cited
2007
Our final solution (RMSE=0.8712) consists of blending 107 individual results. Since many of these results are close variants, we… (More)
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Review
2007
Review
2007
In October, 2006 Netflix released a dataset containing 100 million anonymous movie ratings and challenged the data mining… (More)
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Highly Cited
2006
Highly Cited
2006
As part of the Netflix Prize contest, Netflix recently released a dataset containing movie ratings of a significant fraction of… (More)
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2006
2006
This paper analyzes the performance of various KNNs techniques as applied to the netflix collaborative filtering problem. 
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