Netflix goes ‘beyond five stars’ in a more detailed explanation of recommendations

Netflix goes 'beyond five stars' in a more detailed explanation of recommendations

The Netflix Tech Blog produced part one of a deep dive into how its recommendations work back in April and now the team is back with the other half. If you're among the many wondering why certain movies get pushed to the front of your recommendations and others don't, the key is their attempt to predict, mostly based on data from other users, what you will both play and enjoy. The most interesting bit we found? There's a lot more at play here than just popularity, as one graph shows ratings plus the team's other optimizations improving rankings over the baseline by 200+ percent. Data parsing heads should definitely dig hearing about logistic regression, elastic nets and matrix factorization (job applications are accepted at the end if you make it that far), while those of us that fall asleep when the spreadsheets come out can probably focus on the broader strokes of Netflix's testing methodology and approach.

Netflix goes 'beyond five stars' in a more detailed explanation of recommendations originally appeared on Engadget on Fri, 22 Jun 2012 00:59:00 EDT. Please see our terms for use of feeds.

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Netflix explains its recommendation system, can’t find a reason for Adam Sandler’s last movie

Netflix explains its recommendation system, can't find a reason for Adam Sandler's last movie
In case you've been wondering why Netflix tends to recommend the movies it does, there's a post on the company's Tech Blog breaking down the various levels of its system. Remember the Netflix Prize contest? Teams of researchers produced competing algorithms capable of more accurately predicting how members would rate movies, but while some of the early winning efforts are still in use, the million dollar solution was never implemented because the potential gains were too small to justify the engineering effort needed. Additionally, while Netflix still hasn't implemented individual profiles for household members yet, the blog indicates it does try to recommend something for everyone, seeking both accuracy and diversity -- which may explain some of more out there picks in our personal "recommended for you" list. Where available (read: outside the US) Facebook integration plays a part too, as well as a variety of information used to find movies similar to those previously viewed. The proof of how all these parts come together is ultimately judged by the viewers, so while we wait for part two of the post with more data to pore over -- is Netflix managing to accurately pull any flicks you want to watch out of its catalog?

Netflix explains its recommendation system, can't find a reason for Adam Sandler's last movie originally appeared on Engadget on Sun, 08 Apr 2012 11:06:00 EDT. Please see our terms for use of feeds.

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