DeepMatch

DeepMatch is a deep matching model library for recommendations & advertising. 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() .

Installation and compatibility

DeepMatch does not pin or install TensorFlow for you. Install a TensorFlow build that matches your Python, NumPy, CPU/GPU, and operating system first, then install DeepMatch:

pip install tensorflow
pip install deepmatch

For Python >=3.9, DeepMatch and its dependencies allow modern h5py releases with h5py>=3.7.0. If TensorFlow reports a NumPy conflict, follow the TensorFlow requirement for your selected TensorFlow release, for example using numpy<2 when required by TensorFlow.

Use public tensorflow.keras APIs in your own code and examples. Avoid mixing tensorflow.python.keras with tensorflow.keras, because tensorflow.python.* is private TensorFlow API and can break model serialization or optimizer/metric loading across TensorFlow versions.

Let's Get Started! or Run examples !

Models List

Model	Paper
FM	[ICDM 2010]Factorization Machines
DSSM	[CIKM 2013]Deep Structured Semantic Models for Web Search using Clickthrough Data
YoutubeDNN	[RecSys 2016]Deep Neural Networks for YouTube Recommendations
NCF	[WWW 2017]Neural Collaborative Filtering
SDM	[CIKM 2019]SDM: Sequential Deep Matching Model for Online Large-scale Recommender System
MIND	[CIKM 2019]Multi-interest network with dynamic routing for recommendation at Tmall
COMIREC	[KDD 2020]Controllable Multi-Interest Framework for Recommendation

Contributors(welcome to join us!)

​ ​ Shen Weichen ​ Alibaba Group ​

Wang Zhe ​ Baidu Inc. ​

​ ​ Chen Leihui ​ Alibaba Group ​

​ LeoCai ByteDance ​

​ ​ Li Yuan Tencent ​

​ ​ Yang Jieyu Ant Group ​

​ ​ Meng Yifan DeepCTR ​

DisscussionGroup

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