Abstract

This demonstration proposes StreamRec, a novel approach to building recommender systems that leverages a stream processing system capable of handling an end-to-end recommendation process in order to produce real-time recommendations. We demonstrate several popular collaborative filtering recommendation methods within StreamRec by providing an application scenario that uses StreamRec as the underlying recommendation engine.

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