This Jupyter Notebook outlines my process as I create a movie recommendation system using matrix factorization. I use the public 100k MovieLens dataset.
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Oct 2, 2018 - Jupyter Notebook
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alternating-least-squares
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This project walks through how you can create recommendations using Apache Spark machine learning. There are a number of jupyter notebooks that you can run on IBM Data Science Experience, and there a live demo of a movie recommendation web application you can interact with. The demo also uses IBM Message Hub (kafka) to push application events to…
redis
machine-learning
kafka
spark
hive
hadoop
notebook
jupyter-notebook
collaborative-filtering
messagehub
spark-streaming
bluemix
bokeh
ibm-bluemix
cloudant
dsx
alternating-least-squares
python-flask-application
biginsights
ibm-biginsights
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Updated
Apr 17, 2023 - Jupyter Notebook
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