Answer / Rakhi Verma

The Netflix Prize was a public competition held between 2006 and 2009 to develop a movie recommendation algorithm that could improve upon Netflix's existing system by at least 10%. To approach this competition, teams would focus on developing sophisticated collaborative filtering algorithms based on user ratings data. Techniques used included matrix factorization methods such as singular value decomposition (SVD), alternating least squares (ALS) optimization, and latent Dirichlet allocation (LDA). Teams also experimented with hybrid approaches that combined content-based and collaborative filtering techniques.

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