# DeepMatch **Repository Path**: mirrors_shenweichen/DeepMatch ## Basic Information - **Project Name**: DeepMatch - **Description**: A deep matching model library for recommendations & advertising. It's easy to train models and to export representation vectors which can be used for ANN search. - **Primary Language**: Unknown - **License**: Apache-2.0 - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 1 - **Forks**: 1 - **Created**: 2021-10-22 - **Last Updated**: 2026-01-11 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # DeepMatch [](https://pypi.org/project/deepmatch) [](https://pypi.org/project/deepmatch) [](https://pepy.tech/project/deepmatch) [](https://pypi.org/project/deepmatch) [](https://github.com/shenweichen/deepmatch/issues) [](https://deepmatch.readthedocs.io/)  [](https://codecov.io/gh/shenweichen/DeepMatch) [](https://www.codacy.com/gh/shenweichen/DeepMatch/dashboard?utm_source=github.com&utm_medium=referral&utm_content=shenweichen/DeepMatch&utm_campaign=Badge_Grade) [](https://github.com/shenweichen/DeepMatch#disscussiongroup) [](https://github.com/shenweichen/deepmatch/blob/master/LICENSE) 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()` . Let's [**Get Started!**](https://deepmatch.readthedocs.io/en/latest/Quick-Start.html) or [**Run examples**](./examples/colab_MovieLen1M_YoutubeDNN.ipynb) ! ## Models List | Model | Paper | | :------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------- | | FM | [ICDM 2010][Factorization Machines](https://www.researchgate.net/publication/220766482_Factorization_Machines) | | DSSM | [CIKM 2013][Deep Structured Semantic Models for Web Search using Clickthrough Data](https://www.microsoft.com/en-us/research/publication/learning-deep-structured-semantic-models-for-web-search-using-clickthrough-data/) | | YoutubeDNN | [RecSys 2016][Deep Neural Networks for YouTube Recommendations](https://www.researchgate.net/publication/307573656_Deep_Neural_Networks_for_YouTube_Recommendations) | | NCF | [WWW 2017][Neural Collaborative Filtering](https://arxiv.org/abs/1708.05031) | | SDM | [CIKM 2019][SDM: Sequential Deep Matching Model for Online Large-scale Recommender System](https://arxiv.org/abs/1909.00385) | | MIND | [CIKM 2019][Multi-interest network with dynamic routing for recommendation at Tmall](https://arxiv.org/pdf/1904.08030) | | COMIREC | [KDD 2020][Controllable Multi-Interest Framework for Recommendation](https://arxiv.org/pdf/2005.09347.pdf) | ## Contributors([welcome to join us!](./CONTRIBUTING.md))
![]() Shen Weichen Alibaba Group |
![]() Wang Zhe Baidu Inc. |
![]() Chen Leihui Alibaba Group |
![]() LeoCai ByteDance |
![]() Li Yuan Tencent |
![]() Yang Jieyu Ant Group |
![]() Meng Yifan DeepCTR |