April 2016
Intermediate to advanced
336 pages
7h 21m
Portuguese (Portugal, Brazil)
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CAPÍTULO 22
Sistemas Recomendadores
Oh natureza, natureza, por que és tão desonesta, para enviares homens com falsasrecomendações para o mundo!
—Henry Fielding
Outro problema de dados comum é produzir algum tipo de recomendação. A Netflix recomenda filmes que você poderia querer assistir. A Amazon recomenda produtos que você poderia querer comprar. O Twitter recomenda usuários para você seguir. Neste capítulo, veremos várias formas de usar dados para fazer recomendações.
Em particular, veremos o conjunto de dados de users_interests que usamos anteriormente:
users_interests = [
["Hadoop", "Big Data", "HBase", "Java", "Spark", "Storm", "Cassandra"],
["NoSQL", "MongoDB", "Cassandra", "HBase", "Postgres"],
["Python", "scikit-learn", "scipy", "numpy", ...
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.