{ "info": { "author": "rixwew", "author_email": "rixwew@gmail.com", "bugtrack_url": null, "classifiers": [], "description": "# 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| 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&Deep | [HT Cheng, et al. Wide & 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 & 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\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", "description_content_type": "text/markdown", "docs_url": null, "download_url": "", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "https://github.com/rixwew/torchfm", "keywords": "", "license": "", "maintainer": "", "maintainer_email": "", "name": "torchfm", "package_url": "https://pypi.org/project/torchfm/", "platform": "", "project_url": "https://pypi.org/project/torchfm/", "project_urls": { "Homepage": "https://github.com/rixwew/torchfm" }, "release_url": "https://pypi.org/project/torchfm/0.6/", "requires_dist": null, "requires_python": "", "summary": "PyTorch implementation of Factorization Machine Models", "version": "0.6" }, "last_serial": 5882721, "releases": { "0.2": [ { "comment_text": "", "digests": { "md5": "9b7105e188f644b60bb86e63e97d3ca5", "sha256": "f06ea57fa8bb69fe77b6515fea5ffd7642b8cb0663adc6ef35740970c554c1f7" }, "downloads": -1, "filename": "torchfm-0.2-py3-none-any.whl", "has_sig": false, "md5_digest": "9b7105e188f644b60bb86e63e97d3ca5", "packagetype": "bdist_wheel", "python_version": "py3", "requires_python": null, "size": 16939, "upload_time": "2019-05-28T16:10:31", "url": "https://files.pythonhosted.org/packages/84/35/2e94437b55f1ae0350e98449212f5fa8a7dd91ff08bf54617ae71be351ab/torchfm-0.2-py3-none-any.whl" } ], "0.3": [ { "comment_text": "", "digests": { "md5": "16015b26625d965f659497e693daf74a", "sha256": "a6d0a0b1aa0c917e780023b777cb57d14f098a534107441ffaae29f5ee52c26e" }, "downloads": -1, "filename": "torchfm-0.3.tar.gz", "has_sig": false, "md5_digest": "16015b26625d965f659497e693daf74a", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 8651, "upload_time": "2019-09-13T04:22:32", "url": "https://files.pythonhosted.org/packages/0c/8a/9e5702eb17bafd9ec74b300f923d79f1d7a6731a3b98160454690f5bda65/torchfm-0.3.tar.gz" } ], "0.4": [ { "comment_text": "", "digests": { "md5": "dffcf1719556cdd81b0e7edc4c5c6534", "sha256": "5f915e1183da1bfe84e0bbe93c01a3694d7300e83e7629d7f938ad7fb90e3d96" }, "downloads": -1, "filename": "torchfm-0.4.tar.gz", "has_sig": false, "md5_digest": "dffcf1719556cdd81b0e7edc4c5c6534", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 8642, "upload_time": "2019-09-13T05:07:10", "url": "https://files.pythonhosted.org/packages/be/b4/efff6bd29a1fecafb9f901de326458541dcf159fa9c4a972d0f6155422a9/torchfm-0.4.tar.gz" } ], "0.5": [ { "comment_text": "", "digests": { "md5": "3ca070233e4c68cc49ebe61714ac82e0", "sha256": "1c837a2a4ffbcd8570d21b484f9e15ce084d056ffd13723b811e94cce0f5f34e" }, "downloads": -1, "filename": "torchfm-0.5.tar.gz", "has_sig": false, "md5_digest": "3ca070233e4c68cc49ebe61714ac82e0", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 8656, "upload_time": "2019-09-17T05:42:24", "url": "https://files.pythonhosted.org/packages/80/71/d645ef86d0ca5e550837bfadb59e5eb3604dcab26c4f88c3fbc83f4700f4/torchfm-0.5.tar.gz" } ], "0.6": [ { "comment_text": "", "digests": { "md5": "9d499ecb2ef32e5f637444b82b11f9f0", "sha256": "6f371143296cd825e4cd1de49301f1d16d535670073e48a1066ee5b7058c3487" }, "downloads": -1, "filename": "torchfm-0.6.tar.gz", "has_sig": false, "md5_digest": "9d499ecb2ef32e5f637444b82b11f9f0", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 8656, "upload_time": "2019-09-25T02:16:29", "url": "https://files.pythonhosted.org/packages/32/b6/4bd3dbb8991fd8f2f53d53ada52cc8a6193e813f7868fcaf7154bca93ad1/torchfm-0.6.tar.gz" } ] }, "urls": [ { "comment_text": "", "digests": { "md5": "9d499ecb2ef32e5f637444b82b11f9f0", "sha256": "6f371143296cd825e4cd1de49301f1d16d535670073e48a1066ee5b7058c3487" }, "downloads": -1, "filename": "torchfm-0.6.tar.gz", "has_sig": false, "md5_digest": "9d499ecb2ef32e5f637444b82b11f9f0", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 8656, "upload_time": "2019-09-25T02:16:29", "url": "https://files.pythonhosted.org/packages/32/b6/4bd3dbb8991fd8f2f53d53ada52cc8a6193e813f7868fcaf7154bca93ad1/torchfm-0.6.tar.gz" } ] }