DeepMatch

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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:

bashCopy
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!)

<table border="0"> <tbody> <tr align="center" > <td> ​ <a href="https://github.com/shenweichen"><img width="70" height="70" src="https://github.com/shenweichen.png?s=40" alt="pic"></a><br> ​ <a href="https://github.com/shenweichen">Shen Weichen</a> ​ <p> Alibaba Group </p>​ </td> <td> <a href="https://github.com/wangzhegeek"><img width="70" height="70" src="https://github.com/wangzhegeek.png?s=40" alt="pic"></a><br> <a href="https://github.com/wangzhegeek">Wang Zhe</a> ​ <p>Baidu Inc. </p>​ </td> <td> ​ <a href="https://github.com/clhchtcjj"><img width="70" height="70" src="https://github.com/clhchtcjj.png?s=40" alt="pic"></a><br> ​ <a href="https://github.com/clhchtcjj">Chen Leihui</a> ​ <p> Alibaba Group </p>​ </td> <td> ​ <a href="https://github.com/LeoCai"><img width="70" height="70" src="https://github.com/LeoCai.png?s=40" alt="pic"></a><br> <a href="https://github.com/LeoCai">LeoCai</a> <p> ByteDance </p>​ </td> <td> ​ <a href="https://github.com/liyuan97"><img width="70" height="70" src="https://github.com/liyuan97.png?s=40" alt="pic"></a><br> ​ <a href="https://github.com/liyuan97">Li Yuan</a> <p> Tencent </p>​ </td> <td> ​ <a href="https://github.com/yangjieyu"><img width="70" height="70" src="https://github.com/yangjieyu.png?s=40" alt="pic"></a><br> ​ <a href="https://github.com/yangjieyu">Yang Jieyu</a> <p> Ant Group </p>​ </td> <td> ​ <a href="https://github.com/zzszmyf"><img width="70" height="70" src="https://github.com/zzszmyf.png?s=40" alt="pic"></a><br> ​ <a href="https://github.com/zzszmyf">Meng Yifan</a> <p> DeepCTR </p>​ </td> </tr> </tbody> </table>

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