Presented By O’Reilly and Cloudera
Make Data Work
September 11, 2018: Training & Tutorials
September 12–13, 2018: Keynotes & Sessions
New York, NY

Building a recommendation engine

Sophie Watson (Red Hat)
2:05pm–2:45pm Wednesday, 09/12/2018
Data engineering and architecture
 Location: 1A 10 Level: Beginner
Average rating: ***..
(3.50, 6 ratings)
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Who is this presentation for?

  • Software engineers and data scientists

What you'll learn

  • Learn how to build a recommendation system using microservices

Description

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.

Photo of Sophie Watson

Sophie Watson

Red Hat

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.