Many of today’s most engaging (and commercially important) applications provide personalized experiences to users. Collaborative filtering algorithms capture the commonality between users and enable applications to make personalized recommendations quickly and efficiently.
The alternating least squares (ALS) algorithm is still deemed the industry standard in collaborative filtering. Sophie Watson demonstrates how to implement ALS using Apache Spark to build your own recommendation engine for cases where recorded data is explicitly given as a rating as well as for cases where the data is less succinct. You’ll learn how to reduce the system’s complexity by splitting the recommendation engine into multiple cooperating services and produce a robust collaborative filtering platform with support for continuous model training.
Join in to gain the knowledge and explore the tools needed to implement your own recommendation system using collaborative filtering and microservices.
Sophie Watson is a senior data scientist at Red Hat, where she helps customers use machine learning to solve business problems in the hybrid cloud. She’s a frequent public speaker on topics including machine learning workflows on Kubernetes, recommendation engines, and machine learning for search. Sophie earned her PhD in Bayesian statistics.
For exhibition and sponsorship opportunities, email strataconf@oreilly.com
For information on trade opportunities with O'Reilly conferences, email partners@oreilly.com
View a complete list of Strata Data Conference contacts
©2018, O'Reilly Media, Inc. • (800) 889-8969 or (707) 827-7019 • Monday-Friday 7:30am-5pm PT • All trademarks and registered trademarks appearing on oreilly.com are the property of their respective owners. • confreg@oreilly.com