{"id":23019850,"url":"https://github.com/hexiangnan/neural_factorization_machine","last_synced_at":"2025-04-05T17:08:08.710Z","repository":{"id":41293704,"uuid":"99130297","full_name":"hexiangnan/neural_factorization_machine","owner":"hexiangnan","description":"TenforFlow Implementation of Neural Factorization Machine","archived":false,"fork":false,"pushed_at":"2020-03-01T09:05:52.000Z","size":20519,"stargazers_count":470,"open_issues_count":13,"forks_count":185,"subscribers_count":18,"default_branch":"master","last_synced_at":"2025-03-29T16:08:35.130Z","etag":null,"topics":["deep-learning","factorization-machines","neural-factorization-machines","recommender-system"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/hexiangnan.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2017-08-02T15:07:12.000Z","updated_at":"2025-03-26T06:13:30.000Z","dependencies_parsed_at":"2022-09-01T15:34:22.364Z","dependency_job_id":null,"html_url":"https://github.com/hexiangnan/neural_factorization_machine","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hexiangnan%2Fneural_factorization_machine","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hexiangnan%2Fneural_factorization_machine/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hexiangnan%2Fneural_factorization_machine/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hexiangnan%2Fneural_factorization_machine/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/hexiangnan","download_url":"https://codeload.github.com/hexiangnan/neural_factorization_machine/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247369952,"owners_count":20927928,"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","factorization-machines","neural-factorization-machines","recommender-system"],"created_at":"2024-12-15T12:07:13.735Z","updated_at":"2025-04-05T17:08:08.692Z","avatar_url":"https://github.com/hexiangnan.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Neural Factorization Machines\n\nThis is our implementation for the paper:\n\nXiangnan He and Tat-Seng Chua (2017). [Neural Factorization Machines for Sparse Predictive Analytics.](http://www.comp.nus.edu.sg/~xiangnan/papers/sigir17-nfm.pdf) In Proceedings of SIGIR '17, Shinjuku, Tokyo,\nJapan, August 07-11, 2017.\n\nWe have additionally released our TensorFlow implementation of Factorization Machines under our proposed neural network framework. \n\n**Please cite our SIGIR'17 paper if you use our codes. Thanks!** \n\nAuthor: Dr. Xiangnan He (http://www.comp.nus.edu.sg/~xiangnan/)\n\n## Example to run the codes.\n\n```\npython NeuralFM.py --dataset frappe --hidden_factor 64 --layers [64] --keep_prob [0.8,0.5] --loss_type square_loss --activation relu --pretrain 0 --optimizer AdagradOptimizer --lr 0.05 --batch_norm 1 --verbose 1 --early_stop 1 --epoch 200\n```\nThe instruction of commands has been clearly stated in the codes (see the  parse_args function). \n\nThe current implementation supports two tasks: regression and binary classification. The regression task optimizes RMSE, and the binary classification task optimizes Log Loss. \n\n### Dataset\nWe use the same input format as the LibFM toolkit (http://www.libfm.org/). \n\nSplit the data to train/test/validation files to run the codes directly (examples see data/frappe/). \n\n\n\nLast Update Date: May 11, 2017\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhexiangnan%2Fneural_factorization_machine","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhexiangnan%2Fneural_factorization_machine","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhexiangnan%2Fneural_factorization_machine/lists"}