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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… Expand
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Papers overview

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Highly Cited
2016
Highly Cited
2016
How does algorithmic information processing affect the meaning of the word culture, and, by extension, cultural practice? We… Expand
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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 historical… Expand
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Highly Cited
2008
Highly Cited
2008
Our RMSE=0.8643 solution is a linear blend of over 100 results. Some of them are new to this year, whereas many others belong to… Expand
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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… Expand
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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… Expand
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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… Expand
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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… Expand
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Review
2007
Review
2007
Netflix released a dataset containing 100 million anonymous movie ratings and challenged the data mining, machine learning and… Expand
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Highly Cited
2006
Highly Cited
2006
We present a new class of statistical de-anonymization attacks against high-dimensional micro-data, such as individual… Expand
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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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