{"id":21897871,"url":"https://github.com/longmaoteamtf/deep_recommenders","last_synced_at":"2025-04-05T22:05:50.447Z","repository":{"id":37624659,"uuid":"249671952","full_name":"LongmaoTeamTf/deep_recommenders","owner":"LongmaoTeamTf","description":"Deep Recommenders","archived":false,"fork":false,"pushed_at":"2023-07-06T22:48:21.000Z","size":2407,"stargazers_count":327,"open_issues_count":5,"forks_count":108,"subscribers_count":6,"default_branch":"master","last_synced_at":"2025-03-29T20:09:19.767Z","etag":null,"topics":["deep-learning","multi-task-learning","ranking","recommendation-system","retrieval"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/LongmaoTeamTf.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2020-03-24T09:59:26.000Z","updated_at":"2025-03-04T16:35:20.000Z","dependencies_parsed_at":"2024-12-26T00:06:17.206Z","dependency_job_id":null,"html_url":"https://github.com/LongmaoTeamTf/deep_recommenders","commit_stats":null,"previous_names":[],"tags_count":5,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/LongmaoTeamTf%2Fdeep_recommenders","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/LongmaoTeamTf%2Fdeep_recommenders/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/LongmaoTeamTf%2Fdeep_recommenders/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/LongmaoTeamTf%2Fdeep_recommenders/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/LongmaoTeamTf","download_url":"https://codeload.github.com/LongmaoTeamTf/deep_recommenders/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247406085,"owners_count":20933803,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["deep-learning","multi-task-learning","ranking","recommendation-system","retrieval"],"created_at":"2024-11-28T14:20:08.671Z","updated_at":"2025-04-05T22:05:50.428Z","avatar_url":"https://github.com/LongmaoTeamTf.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Deep Recommenders\n[![Python](https://img.shields.io/badge/python-3.7_|_3.8-brightgreen)](requirements.txt)\n[![TensorFlow](https://img.shields.io/badge/tensorflow-1.15_|_2.0+-brightgreen)](requirements.txt)\n[![Codacy Badge](https://app.codacy.com/project/badge/Grade/c4b6335acf254697b80714b81e8154d7)](https://www.codacy.com/gh/LongmaoTeamTf/deep_recommenders/dashboard?utm_source=github.com\u0026amp;utm_medium=referral\u0026amp;utm_content=LongmaoTeamTf/deep_recommenders\u0026amp;utm_campaign=Badge_Grade)\n\n[![CodeQL](https://github.com/LongmaoTeamTf/deep_recommenders/actions/workflows/codeql-analysis.yml/badge.svg)](https://github.com/LongmaoTeamTf/deep_recommenders/actions/workflows/codeql-analysis.yml)\n[![CI](https://github.com/LongmaoTeamTf/deep_recommenders/actions/workflows/continuous_integration.yml/badge.svg)](https://github.com/LongmaoTeamTf/deep_recommenders/actions/workflows/continuous_integration.yml)\n[![codecov](https://codecov.io/gh/LongmaoTeamTf/deep_recommenders/branch/master/graph/badge.svg?token=X9VQCCLJJX)](https://codecov.io/gh/LongmaoTeamTf/deep_recommenders)\n[![License](https://img.shields.io/badge/license-Apache_2.0-green)](LICENSE)\n\nDeep Recommenders is an open-source recommendation system algorithm library \nbuilt by `tf.estimator` and `tf.keras` that the advanced APIs of TensorFlow.\n\n🤗️ This Library mainly used for self-learning and improvement, \nbut also hope to help friends and classmates who are interested in the recommendation system to make progress together!\n\n## Models\n\n### Ranking\n\n  - **FM**\n    [[Estimator]](examples/train_fm_on_movielens_estimator.py)\n    [\u003csub\u003e\n      *Factorization Machines, Osaka, 2010*\n    \u003c/sub\u003e](https://www.csie.ntu.edu.tw/~b97053/paper/Rendle2010FM.pdf)\n  - **FFM**\n    [\u003csub\u003e\n      *Field-aware Factorization Machines for CTR Prediction, RecSys, 2016*\n    \u003c/sub\u003e](https://www.csie.ntu.edu.tw/~cjlin/papers/ffm.pdf)\n  - **LS-PLM**\n    [\u003csub\u003e\n      *Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction, Alibaba, 2017*\n    \u003c/sub\u003e](https://arxiv.org/pdf/1704.05194.pdf)\n  - **WDL**\n    [[Estimator]](examples/train_wdl_on_movielens_estimator.py)\n    [\u003csub\u003e\n      *Wide \u0026 Deep Learning for Recommender Systems, Google, DLRS, 2016*\n    \u003c/sub\u003e](https://arxiv.org/abs/1606.07792)\n  - **PNN**\n    [\u003csub\u003e\n      *Product-based Neural Networks for User Response Prediction, IEEE, 2016*\n    \u003c/sub\u003e](https://arxiv.org/abs/1611.00144)\n  - **FNN**\n    [[Estimator]](examples/train_fnn_on_movielens_estimator.py)\n    [\u003csub\u003e\n      *Deep Learning over Multi-field Categorical Data: A Case Study on User Response Prediction, RayCloud, ECIR, 2016*\n    \u003c/sub\u003e](https://arxiv.org/abs/1601.02376)\n  - **NFM**\n    [\u003csub\u003e\n      *Neural Factorization Machines for Sparse Predictive Analytics, SIGIR, 2017*\n    \u003c/sub\u003e](https://arxiv.org/pdf/1708.05027.pdf)\n  - **AFM** \n    [\u003csub\u003e\n      *Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks, IJCAI, 2017*\n    \u003c/sub\u003e](https://www.ijcai.org/proceedings/2017/0435.pdf)\n  - **DeepFM** \n    [[Estimator]](examples/train_deepfm_on_movielens_estimator.py)\n    [[Keras]](examples/train_deepfm_on_movielens_keras.py) \n    [\u003csub\u003e\n      *DeepFM: A Factorization-Machine based Neural Network for CTR Prediction, Huawei, IJCAI, 2017*\n    \u003c/sub\u003e](https://www.ijcai.org/proceedings/2017/0239.pdf)\n  - **DCN** \n    [\u003csub\u003e\n      *Deep \u0026 Cross Network for Ad Click Predictions, Google, KDD, 2017*\n    \u003c/sub\u003e](https://arxiv.org/abs/1708.05123)\n  - **xDeepFM** \n    [\u003csub\u003e\n      *xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems, Microsoft, KDD, 2018*\n    \u003c/sub\u003e](https://arxiv.org/pdf/1803.05170.pdf)\n  - **DIN** \n    [\u003csub\u003e\n      *Deep Interest Network for Click-Through Rate Prediction, Alibaba, KDD, 2018*\n    \u003c/sub\u003e](https://arxiv.org/abs/1706.06978)   \n  - **DIEN**\n    [\u003csub\u003e\n      *Deep Interest Evolution Network for Click-Through Rate Prediction, Alibaba, AAAI, 2019*\n    \u003c/sub\u003e](https://arxiv.org/abs/1809.03672)\n  - **DLRM**\n    [\u003csub\u003e\n      *Deep Learning Recommendation Model for Personalization and Recommendation Systems, Facebook, 2019*\n    \u003c/sub\u003e](https://arxiv.org/abs/1906.00091)\n\n### Retrieval\n\n  - **DSSM**\n    [\u003csub\u003e\n      *Learning Deep Structured Semantic Models for Web Search using Clickthrough Data, Microsoft, CIKM, 2013*\n    \u003c/sub\u003e](https://dl.acm.org/doi/10.1145/2505515.2505665)\n  - **YoutubeNet**\n    [\u003csub\u003e\n      *Deep Neural Networks for YouTube Recommendations, Google, RecSys, 2016*\n    \u003c/sub\u003e](https://static.googleusercontent.com/media/research.google.com/zh-CN//pubs/archive/45530.pdf)\n  - **SBCNM** \n    [\u003csub\u003e\n      *Sampling-Bias-Corrected Neural Modeling for Large Corpus Item Recommendations, Google, RecSys, 2019*\n    \u003c/sub\u003e](https://dl.acm.org/doi/10.1145/3298689.3346996)\n  - **EBR**\n    [\u003csub\u003e\n      *Embedding-based Retrieval in Facebook Search, Facebook, KDD, 2020*\n    \u003c/sub\u003e](https://arxiv.org/abs/2006.11632)\n  - **Item2Vec**\n    [\u003csub\u003e\n      *Item2Vec: Neural Item Embedding for Collaborative Filtering, Microsoft, MLSP, 2016*\n    \u003c/sub\u003e](https://arxiv.org/vc/arxiv/papers/1603/1603.04259v2.pdf)\n  - **Airbnb**\n    [\u003csub\u003e\n      *Real-time Personalization using Embeddings for Search Ranking at Airbnb, Airbnb, KDD, 2018*\n    \u003c/sub\u003e](https://dl.acm.org/doi/10.1145/3219819.3219885)\n  - **DeepWalk**\n    [\u003csub\u003e\n      *DeepWalk: Online Learning of Social Representations, StonyBrook, KDD, 2014*\n    \u003c/sub\u003e](https://arxiv.org/abs/1403.6652)\n  - **EGES**\n    [\u003csub\u003e\n      *Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba, Alibaba, KDD, 2018*\n    \u003c/sub\u003e](https://arxiv.org/abs/1803.02349)\n  - **GCN**\n    [[Keras]](experiments/gcn.ipynb)\n    [\u003csub\u003e\n      *Semi-Supervised Classification with Graph Convolutional Networks, ICLR, 2017*\n    \u003c/sub\u003e](https://arxiv.org/abs/1609.02907)       \n  - **GraphSAGE**\n    [\u003csub\u003e\n      *Inductive Representation Learning on Large Graphs, NIPS, 2017*\n    \u003c/sub\u003e](https://arxiv.org/abs/1706.02216)\n  - **PinSage**\n    [\u003csub\u003e\n      *Graph Convolutional Neural Networks for Web-Scale Recommender Systems, Pinterest, KDD, 2018*\n    \u003c/sub\u003e](https://arxiv.org/abs/1806.01973)\n  - **IntentGC**\n    [\u003csub\u003e\n      *IntentGC: a Scalable Graph Convolution Framework Fusing Heterogeneous Information for Recommendation, Alibaba, KDD, 2019*\n    \u003c/sub\u003e](https://arxiv.org/abs/1907.12377)\n  - **GraphTR**\n    [\u003csub\u003e\n      *Graph Neural Network for Tag Ranking in Tag-enhanced Video Recommendation, Tencent, CIKM, 2020*\n    \u003c/sub\u003e](https://dl.acm.org/doi/abs/10.1145/3340531.3416021)\n    \n### Multi-task learning\n\n  - **MMoE**\n    [[Estimator]](examples/train_mmoe_on_synthetic_estimator.py)\n    [\u003csub\u003e\n      *Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts, Google, KDD, 2018*\n    \u003c/sub\u003e](https://dl.acm.org/doi/pdf/10.1145/3219819.3220007)\n  - **ESMM**\n    [\u003csub\u003e\n      *Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate, Alibaba, SIGIR, 2018*\n    \u003c/sub\u003e](https://arxiv.org/pdf/1804.07931.pdf)\n\n### NLP\n\n  - **Word2Vec**\n    [\u003csub\u003e\n      *Distributed Representations of Words and Phrases and their Compositionality, Google, NIPS, 2013*\n    \u003c/sub\u003e](https://papers.nips.cc/paper/2013/file/9aa42b31882ec039965f3c4923ce901b-Paper.pdf)\n\n  - **Transformer**\n    [[Keras]](experiments/transformer.ipynb)\n    [\u003csub\u003e\n      *Attention Is All You Need, Google, NeurlPS, 2017*\n    \u003c/sub\u003e](https://arxiv.org/abs/1706.03762)\n\n  - **BERT**\n    [\u003csub\u003e\n      *BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, Google, NAACL, 2019*\n    \u003c/sub\u003e](https://arxiv.org/abs/1810.04805)\n\n## Supports\n\n[1.15-passing]: https://img.shields.io/badge/1.15-passing-brightgreen\n[1.15-failing]: https://img.shields.io/badge/1.15-failing-red\n[2.0+-passing]: https://img.shields.io/badge/2.0+-passing-brightgreen\n[2.3+-passing]: https://img.shields.io/badge/2.3+-passing-brightgreen\n\n| Modules | TensorFlow |\n| ------- | ---------------- |\n| *deep_recommenders.estimator* | ![1.15-passing]\u003cbr\u003e![2.0+-passing]\n| *deep_recommenders.keras* | ![1.15-failing]\u003cbr\u003e![2.3+-passing]\n\n## License\n[Apache License 2.0](LICENSE)","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flongmaoteamtf%2Fdeep_recommenders","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Flongmaoteamtf%2Fdeep_recommenders","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flongmaoteamtf%2Fdeep_recommenders/lists"}