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by Rich Hagarty, Nick Pentreath | Updated March 28, 2019 - Published October 31, 2017
Artificial intelligenceData science
Recommendation engines are among the most well known, widely used and highest-value use cases for applying machine learning. Despite this, while there are many resources available for the basics of training a recommendation model, there are relatively few that explain how to actually deploy these models to create a large-scale recommender system.
This developer pattern demonstrates the key elements of creating a recommender system by using Apache Spark and Elasticsearch. A Jupyter Notebook shows you how to use Spark for training a collaborative filtering recommendation model from ratings data stored in Elasticsearch, saving the model factors to Elasticsearch, then using Elasticsearch to serve real-time recommendations by using the model.
Upon completion, you’ll know how to:
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