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7 models· Graph and Network Models, Recommendation Systems

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by Meta · 0 reviews

An open-source deep learning architecture for recommendation systems that combines dense and sparse features through embedding tables, interaction layers, and MLP components, used for CTR prediction and personalization.

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GraphSAGE

by Stanford University · 0 reviews

An inductive representation learning framework for large graphs that samples and aggregates node features from local neighborhoods, enabling embeddings for unseen nodes.

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Graph Attention Networks (GAT)

by Mila / University of Cambridge · 0 reviews

A neural network architecture using masked self-attention on graph-structured data, learning different weights for different neighbor nodes.

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Deep Graph Library (DGL)

by Amazon Web Services · 0 reviews

A framework for deep learning on graphs supporting PyTorch and TensorFlow, with optimized message passing primitives and distributed training.

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libFM

by Steffen Rendle · 0 reviews

A supervised learning implementation of factorization machines for classification, regression, and ranking that captures feature interactions, widely used in recommendation systems.

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DeepFM

by Huawei Noah's Ark Lab · 0 reviews

An end-to-end wide-and-deep learning framework combining factorization machines and neural networks for click-through-rate prediction.

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