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Recommendation Engines · Awesome GitHub Repositories

3 repos

Awesome GitHub RepositoriesRecommendation Engines

Algorithms and pipelines that predict and rank items to provide personalized content suggestions to users.

Explore 3 awesome GitHub repositories matching artificial intelligence & ml · Recommendation Engines. Refine with filters or upvote what's useful.

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Awesome Recommendation Engines GitHub Repositories

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  • vinta/awesome-python

    vinta/awesome-python

    283,687GitHubView on GitHub↗

    This project is a comprehensive, community-curated directory that organizes a vast landscape of Python software libraries, frameworks, and tools. It serves as a centralized knowledge base designed to facilitate ecosystem navigation and accelerate developer discovery across the entire software development lifecycle. Th

    Pythonawesomecollectionspython
  • twitter/the-algorithm

    twitter/the-algorithm

    72,764GitHubView on GitHub↗

    The algorithm is a distributed recommendation engine pipeline designed to construct and serve personalized content timelines. It functions as a multi-stage orchestration layer that aggregates candidate content from diverse social graphs and high-dimensional embedding spaces, processing user interaction data to deliver

    Scala
  • keras-team/keras

    keras-team/keras

    63,858GitHubView on GitHub↗

    Keras is a high-level deep learning framework designed for constructing and training neural networks through the composition of modular, functional layers. It serves as a comprehensive modeling toolkit that provides standardized procedures for defining, evaluating, and deploying complex architectures. By utilizing a di

    Pythondata-sciencedeep-learningjax

Explore sub-tags

  • Candidate Sourcing PipelinesMulti-stage retrieval systems for gathering potential content items.
  • Content Discovery AlgorithmsMechanisms for identifying and surfacing relevant content from outside a user's immediate social or interest graph.
  • Content Ranking ModelsNeural network-based systems that score and order content items by predicted relevance.
  • Embedding-Based RetrievalTechniques for finding relevant items by calculating vector similarity between user and content representations.
  • Graph-Based Content DiscoveryAlgorithms that traverse social or interest-based network connections to surface content beyond a user's immediate circle.
  • Recommendation Engine PipelinesDistributed systems orchestrating candidate generation, ranking models, and filtering logic for content delivery.
  • Social Feed Ranking AlgorithmsModels that rank social media content based on predicted user engagement and network relevance.