snowch/movie-recommender-demo
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 topic where they are consumed by a spark streaming job running on IBM BigInsights (hadoop).
Implements collaborative filtering using Spark MLlib's ALS algorithm, storing movie metadata and user ratings in Cloudant NoSQL while optionally streaming user interactions through Kafka to BigInsights for real-time warehouse updates. The architecture decouples the Flask web frontend from batch recommendation generation in DSX notebooks, enabling independent scaling of the interactive application and the ML pipeline. Targets IBM Cloud's Bluemix platform with optional integration to on-premises Hadoop clusters via Message Hub for enterprise data warehouse scenarios.
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Jupyter Notebook
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Apache-2.0
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Apr 17, 2023
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