September 2016
Beginner to intermediate
326 pages
6h 47m
English
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A recommendation system with MLlib
Spark's MLlib implements a collaborative filtering algorithm called Alternating Least Squares (ALS) to build recommendation systems.
ALS models the rating matrix (R) as the multiplication of a low-rank user (U) and product (V) factors, and learns these factors by minimizing the reconstruction error of the observed ratings. The unknown ratings can subsequently be computed by multiplying these factors. In this way, we can recommend products based on the predicted ratings. Refer to the following quote at https://databricks.com/blog/2014/07/23/scalable-collaborative-filtering-with-spark-mllib.html:
"ALS is an iterative algorithm. In each iteration, the algorithm alternatively fixes one factor matrix and solves for ...
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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.