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
- Github Discussions
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| 公众号：浅梦学习笔记 | 微信：deepctrbot | 学习小组 加入 主题集合 |
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1 maintainer
Metadata
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Release files for deepmatch 0.3.2
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| deepmatch-0.3.2.tar.gz | 25.6 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| deepmatch-0.3.2-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 57.9 kB
Release files / deepmatch-0.3.2.tar.gz
| Download URL | deepmatch-0.3.2.tar.gz |
|---|---|
| Size | 25.6 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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Release files / deepmatch-0.3.2-py3-none-any.whl
| Download URL | deepmatch-0.3.2-py3-none-any.whl |
|---|---|
| Size | 32.3 kB |
| Tags | Python 3 |
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SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
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twine/6.2.0 CPython/3.13.5
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