{"id":13677150,"url":"https://github.com/rixwew/pytorch-fm","last_synced_at":"2026-03-17T02:02:44.975Z","repository":{"id":37549661,"uuid":"188970384","full_name":"rixwew/pytorch-fm","owner":"rixwew","description":"Factorization Machine models in PyTorch","archived":false,"fork":false,"pushed_at":"2024-04-08T15:25:40.000Z","size":5837,"stargazers_count":1057,"open_issues_count":19,"forks_count":227,"subscribers_count":16,"default_branch":"master","last_synced_at":"2025-04-01T15:08:34.180Z","etag":null,"topics":["autoint","avazu-dataset","collaborative-filtering","criteo-dataset","ctr-prediction","dcn","deepfm","factorization-machines","ffm","fm","fnfm","hofm","movielens-dataset","neural-collaborative-filtering","nfm","pnn","pytorch","xdeepfm"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/rixwew.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":"2019-05-28T06:46:31.000Z","updated_at":"2025-03-25T12:54:36.000Z","dependencies_parsed_at":"2024-06-18T21:18:16.694Z","dependency_job_id":"270d2186-227d-4568-9f05-1c15243ba481","html_url":"https://github.com/rixwew/pytorch-fm","commit_stats":null,"previous_names":["rixwew/torchfm"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rixwew%2Fpytorch-fm","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rixwew%2Fpytorch-fm/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rixwew%2Fpytorch-fm/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rixwew%2Fpytorch-fm/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/rixwew","download_url":"https://codeload.github.com/rixwew/pytorch-fm/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247878022,"owners_count":21011158,"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":["autoint","avazu-dataset","collaborative-filtering","criteo-dataset","ctr-prediction","dcn","deepfm","factorization-machines","ffm","fm","fnfm","hofm","movielens-dataset","neural-collaborative-filtering","nfm","pnn","pytorch","xdeepfm"],"created_at":"2024-08-02T13:00:37.636Z","updated_at":"2026-03-17T02:02:39.942Z","avatar_url":"https://github.com/rixwew.png","language":"Python","funding_links":[],"categories":["Python"],"sub_categories":[],"readme":"# Factorization Machine models in PyTorch\n  \nThis package provides a PyTorch implementation of factorization machine models and common datasets in CTR prediction.\n\n\n## Available Datasets\n\n* [MovieLens Dataset](https://grouplens.org/datasets/movielens)\n* [Criteo Display Advertising Challenge](https://www.kaggle.com/c/criteo-display-ad-challenge)\n* [Avazu Click-Through Rate Prediction](https://www.kaggle.com/c/avazu-ctr-prediction)\n\n\n## Available Models\n\n| Model | Reference |\n|-------|-----------|\n| Logistic Regression | |\n| Factorization Machine | [S Rendle, Factorization Machines, 2010.](https://www.csie.ntu.edu.tw/~b97053/paper/Rendle2010FM.pdf) |\n| Field-aware Factorization Machine | [Y Juan, et al. Field-aware Factorization Machines for CTR Prediction, 2015.](https://www.csie.ntu.edu.tw/~cjlin/papers/ffm.pdf) |\n| Higher-Order Factorization Machines | [ M Blondel, et al. Higher-Order Factorization Machines, 2016.](https://dl.acm.org/doi/10.5555/3157382.3157473) |\n| Factorization-Supported Neural Network | [W Zhang, et al. Deep Learning over Multi-field Categorical Data - A Case Study on User Response Prediction, 2016.](https://arxiv.org/abs/1601.02376) |\n| Wide\u0026Deep | [HT Cheng, et al. Wide \u0026 Deep Learning for Recommender Systems, 2016.](https://arxiv.org/abs/1606.07792) |\n| Attentional Factorization Machine | [J Xiao, et al. Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks, 2017.](https://arxiv.org/abs/1708.04617) |\n| Neural Factorization Machine | [X He and TS Chua, Neural Factorization Machines for Sparse Predictive Analytics, 2017.](https://arxiv.org/abs/1708.05027) |\n| Neural Collaborative Filtering | [X He, et al. Neural Collaborative Filtering, 2017.](https://arxiv.org/abs/1708.05031) |\n| Field-aware Neural Factorization Machine | [L Zhang, et al. Field-aware Neural Factorization Machine for Click-Through Rate Prediction, 2019.](https://arxiv.org/abs/1902.09096) |\n| Product Neural Network | [Y Qu, et al. Product-based Neural Networks for User Response Prediction, 2016.](https://arxiv.org/abs/1611.00144) |\n| Deep Cross Network | [R Wang, et al. Deep \u0026 Cross Network for Ad Click Predictions, 2017.](https://arxiv.org/abs/1708.05123) |\n| DeepFM | [H Guo, et al. DeepFM: A Factorization-Machine based Neural Network for CTR Prediction, 2017.](https://arxiv.org/abs/1703.04247) |\n| xDeepFM | [J Lian, et al. xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems, 2018.](https://arxiv.org/abs/1803.05170) |\n| AutoInt (Automatic Feature Interaction Model) | [W Song, et al. AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks, 2018.](https://arxiv.org/abs/1810.11921) |\n| AFN(AdaptiveFactorizationNetwork Model) | [Cheng W, et al. Adaptive Factorization Network: Learning Adaptive-Order Feature Interactions, AAAI'20.](https://arxiv.org/pdf/1909.03276.pdf) |\n\nEach model's AUC values are about 0.80 for criteo dataset, and about 0.78 for avazu dataset. (please see [example code](examples/main.py))\n\n\n## Installation\n\n    pip install torchfm\n\n\n## API Documentation\n\nhttps://rixwew.github.io/pytorch-fm\n\n\n## Licence\n\nMIT\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frixwew%2Fpytorch-fm","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frixwew%2Fpytorch-fm","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frixwew%2Fpytorch-fm/lists"}