![]() The data consists of a set of ratings given by users of the MovieLens movie rating system, to various movies. The data you will use comes from MovieLens and is a common benchmark dataset in the recommendations community. This repo contains a Jupyter notebook illustrating how to use Spark for training a collaborative filtering recommendation model from ratings data stored in Elasticsearch, saving the model factors to Elasticsearch, and then using Elasticsearch to serve real-time recommendations using the model. ![]() This Code Pattern demonstrates the key elements of creating such a system, using Apache Spark and Elasticsearch. 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. ![]() Recommendation engines are one of the most well known, widely used and highest value use cases for applying machine learning. Building a Recommender with Apache Spark & Elasticsearch
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