Building a recommendation engine

Recommender systems enhance user experience and business revenue every day. Sophie Watson demonstrates how to develop a robust recommendation engine using a microservice architecture.

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.