How to Build a Machine Learning Recommendation Engine w/ TensorFlow & HarperDB

This page summarizes the projects mentioned and recommended in the original post on dev.to

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  • song-recommender

    Using TensorFlow with HarperDB Custom Functions to create a song recommendation engine.

    Github repo: HarperDB/song-recommender

  • book-recommender

    Using TensorFlow with HarperDB Custom Functions to create a book recommendation engine. (by HarperDB)

    Github repo: HarperDB/book-recommender

  • InfluxDB

    Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

  • Keras

    Deep Learning for humans

    This is where machine learning takes over. Using libraries such as TensorFlow Recommenders with Keras models, it's easy to shape the data in ways that will allow the items and users to be viewed and compared in a multidimensional perspective. Qualitative features such as item categories and user profile attributes can be mapped into mathematical concepts that can be quantitatively compared with one another, ultimately providing new insights and better recommendations.

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

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