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Implementing Collaborative Filtering Algorithm on Netflix Prize Data: Improving Predictive Algorithms

This project involves implementing a collaborative filtering algorithm on a subset of Netflix Prize data, consisting of 1821 movies, 28,978 users, and 3.25 million ratings. With 11,615 submissions from 1960 teams so far, the goal is to improve predictions and potentially add your own ratings to receive personalized recommendations. The project paper "Empirical Analysis of Predictive Algorithms for Collaborative Filtering" by Breese, Heckerman, and Cadie (UAI-98) provides valuable insights.

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Implementing Collaborative Filtering Algorithm on Netflix Prize Data: Improving Predictive Algorithms

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  1. Second Project • Implement collaborative filtering algorithm • Apply to (subset of) Netflix Prize data • 1821 movies, 28,978 users, 3.25 million ratings (* - *****) • To date: 11,615 submissions from 1960 teams • Try to improve predictions • Optional: Add your ratings & get recommendations • Paper: Breese, Heckerman & Cadie, “Empirical Analysis of Predictive Algorithms for Collaborative Filtering” (UAI-98)

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