Tag Archives: recommendation engines

I feel vindicated in several ways by the Netflix Engineering team’s recent blog post explaining what they did with the results of the Netflix Prize. What they wrote confirms what I’ve been saying about recommendations as well as my experience … Continue reading

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Why recommendation engines matter

“The Internet makes everything available, but mere availability is meaningless if the products remain unknown to potential buyers.” (possibly attributable to Gavin Potter, a Netflix Prize contestant; possibly to Jordan Ellenberg, who writes for Slate and Wired)

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Movie Ratings

Another unmeasurable distance is ★★★ movie ratings. Movie ratings are drawn from the set {★,★★,★★★,★★★★,★★★★★} and related by the total ordering >: ★★★★★ > ★★★★ ★★★★ > ★★★ ★★★ > ★★ ★★ > ★ and the transitivity of > gives … Continue reading

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Recommendation Engines

The Netflix Prize was awarded to the team with the algorithm that most accurately guessed people’s movie tastes. Accurate, according to some measure: root-mean-squared error, or the L2 norm. In my opinion, that’s the wrong measure of success. Netflix selected … Continue reading

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Recommendation Engines

The Netflix Prize was awarded to the team with the algorithm that most accurately guessed people’s movie tastes. Accurate, according to some measure: root-mean-squared error, or the L₂ norm. In my opinion, that’s the wrong measure of success. Netflix selected … Continue reading

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Facebook EdgeRank Another quasimetric! Actually a pseudo-quasi-metric. Facebook’s news feed decides what status updates to show you based on three factors: affinity u — how much you like somebody (FB’s guess) type of post w — videos and photos are … Continue reading

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