{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/deepfm-an-end-to-end-wide-deep-learning","title":"DeepFM: An End-to-End Wide & Deep Learning Framework for CTR Prediction","arxiv_id":"1804.04950","date":"2018-04-12","proceeding":null,"authors":["Huifeng Guo","Ruiming Tang","Yunming Ye","Zhenguo Li","Xiuqiang He","Zhenhua Dong"],"abstract":"Learning sophisticated feature interactions behind user behaviors is critical\nin maximizing CTR for recommender systems. Despite great progress, existing\nmethods have a strong bias towards low- or high-order interactions, or rely on\nexpertise feature engineering. In this paper, we show that it is possible to\nderive an end-to-end learning model that emphasizes both low- and high-order\nfeature interactions. The proposed framework, DeepFM, combines the power of\nfactorization machines for recommendation and deep learning for feature\nlearning in a new neural network architecture. Compared to the latest Wide &\nDeep model from Google, DeepFM has a shared raw feature input to both its\n\"wide\" and \"deep\" components, with no need of feature engineering besides raw\nfeatures. DeepFM, as a general learning framework, can incorporate various\nnetwork architectures in its deep component. In this paper, we study two\ninstances of DeepFM where its \"deep\" component is DNN and PNN respectively, for\nwhich we denote as DeepFM-D and DeepFM-P. Comprehensive experiments are\nconducted to demonstrate the effectiveness of DeepFM-D and DeepFM-P over the\nexisting models for CTR prediction, on both benchmark data and commercial data.\nWe conduct online A/B test in Huawei App Market, which reveals that DeepFM-D\nleads to more than 10% improvement of click-through rate in the production\nenvironment, compared to a well-engineered LR model. We also covered related\npractice in deploying our framework in Huawei App Market.","url_abs":"http://arxiv.org/abs/1804.04950v2","url_pdf":"http://arxiv.org/pdf/1804.04950v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"deepfm-an-end-to-end-wide-deep-learning","repo_url":"https://github.com/2023-MindSpore-1/ms-code-14/tree/main/DeepFM","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"deepfm-an-end-to-end-wide-deep-learning","repo_url":"https://github.com/DataCanvasIO/DeepTables","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"deepfm-an-end-to-end-wide-deep-learning","repo_url":"https://github.com/MindSpore-paper-code-2/code399/tree/main/DeepFM","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"deepfm-an-end-to-end-wide-deep-learning","repo_url":"https://github.com/MindSpore-paper-code-2/code399/tree/main/Wide_and_Deep","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"deepfm-an-end-to-end-wide-deep-learning","repo_url":"https://github.com/PaddlePaddle/PaddleRec/tree/master/models/rank/deepfm","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"paddle","reach":null},{"paper_slug":"deepfm-an-end-to-end-wide-deep-learning","repo_url":"https://github.com/shenweichen/DeepCTR","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null},{"paper_slug":"deepfm-an-end-to-end-wide-deep-learning","repo_url":"https://github.com/shenweichen/DeepCTR-PyTorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"deepfm-an-end-to-end-wide-deep-learning","repo_url":"https://github.com/tangxyw/RecAlgorithm","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok","spdx":"BSD-2-Clause"}}],"tasks":[{"task_slug":"click-through-rate-prediction","task_name":"Click-Through Rate Prediction"},{"task_slug":"feature-engineering","task_name":"Feature Engineering"},{"task_slug":"recommendation-systems","task_name":"Recommendation Systems"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1804.04950","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1804.04950"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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