Welcome to DeepMatch’s documentation!

DeepMatch is a deep matching model library for recommendations, advertising, and search. It’s easy to train models and to export representation vectors for user and item which can be used for ANN search.You can use any complex model with model.fit() and model.predict() .

Let’s Get Started! or Run examples!

You can read the latest code at https://github.com/shenweichen/DeepMatch

News

07/04/2022 : Support different negative sampling strategies, including inbatch , uniform , frequency , adaptive . Changelog

06/17/2022 : Fix some bugs. Changelog

10/12/2020 : Support different initializers for different embedding weights and loading pretrained embeddings. Changelog

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Home:

• Quick-Start
  • Installation Guide
  • Run examples !!
• Features
  • Feature Columns
  • Models
• Examples
  • Run models on MovieLen1M in Google colab
  • YoutubeDNN/MIND with sampled softmax
  • SDM with sampled softmax
  • DSSM with in batch softmax
  • DSSM with negative sampling
• FAQ
  • 1. Save or load weights/models
  • 2. Set learning rate and use earlystopping
• History

API:

• Models
  • Model Methods
    • compile
    • fit
    • evaluate
    • predict
    • train_on_batch
    • test_on_batch
    • predict_on_batch
    • fit_generator
    • evaluate_generator
    • predict_generator
    • get_layer
  • FM
  • DSSM
  • YoutubeDNN
  • NCF
  • SDM
  • MIND

Indices and tables

• Index
• Module Index
• Search Page