March 2020
Beginner to intermediate
417 pages
11h 9m
English
Content preview from The Practitioner's Guide to Graph Data
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Chapter 10. Recommendations in Development
The Netflix Prize was an open machine learning competition started in 2006. Each team that entered the competition aimed to build an algorithm capable of besting Netflix’s own content rating prediction process. The competition awarded $1 million to the winning team in 2009.
One specific derivative of the Netflix Prize sent waves throughout the graph theory community, a result you are experiencing now as you read this book. The competition ignited the use of graph thinking as a solution for traditionally matrix-based algorithms.
The realization was that it is much easier to explain recommendation systems with a graph than with a matrix representation. Think about it. You have a favorite set of movies, and each of those movies is highly rated by other people. If you look at the other movies liked by those people, you have a list of movies that you may also like. You have a list of movie recommendations.
And you just walked through a graph to find them.
The Netflix Prize1 popularized the idea of using relationships between users and movies to predict and personalize your digital experience. This small idea of thinking about your data like a graph has become one of the main drivers of the rise of graph thinking.
We will bring this idea to life throughout this chapter and Chapter 12. And in case you are wondering, Chapter 11 shows you how we created the graph model you will see in this chapter.
Chapter Preview: Collaborative Filtering for ...
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