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IBM / elasticsearch-spark-recommender

Use Jupyter Notebooks to demonstrate how to build a Recommender with Apache Spark & Elasticsearch
https://developer.ibm.com/code/patterns/build-a-recommender-with-apache-spark-and-elasticsearch/
Apache License 2.0
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Add MovieLens description to intro section #23

Closed MLnick closed 6 years ago

MLnick commented 6 years ago

Resolves #20

cc @rhagarty

MLnick commented 6 years ago

Yup thanks On Mon, 23 Oct 2017 at 17:29, Rich Hagarty notifications@github.com wrote:

@rhagarty requested changes on this pull request.

small nit

In README.md https://github.com/IBM/elasticsearch-spark-recommender/pull/23#discussion_r146304362 :

@@ -7,7 +7,7 @@ Recommendation engines are one of the most well known, widely used and highest v

This developer journey demonstrates the key elements of creating such a system, using Apache Spark and Elasticsearch.

-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 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. The data you will use comes from MovieLens and is a common benchmark dataset in the recommendations community. The data consists of a set ratings given by users of the MovieLens movie rating system, to various movies. It also contains metadata (title and genres) for each movie.

should "consists of a set ratings given ..." be "consists of a set of ratings given ..."?

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