How to Build a Recommender System with Embeddinghub

• featureform

  28 1,674 9.7 Jupyter Notebook

  The Virtual Feature Store. Turn your existing data infrastructure into a feature store.

  

Usually embeddings — dense numerical representations of real-world objects and relationships, expressed as a vector — are stored in database servers such as PostgreSQLEmbedding. However Embeddinghub makes it easier to store your embeddings and load them. You can get started with minimal setup, and it also makes your code look less verbose as compared to, say, building a KNN model using scikit-learn.

• Embeddinghub-Anime-Recommender

  1 1 0.0 Python

If you followed along with this tutorial, you just built a content-based recommendation model to recommend anime. And if you didn’t, the source code for it is right here.

• InfluxDB

  www.influxdata.com sponsored

  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.

  

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