{"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/feature-generation-by-convolutional-neural","title":"Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction","arxiv_id":"1904.04447","date":"2019-04-09","proceeding":null,"authors":["Bin Liu","Ruiming Tang","Yingzhi Chen","Jinkai Yu","Huifeng Guo","Yuzhou Zhang"],"abstract":"Easy-to-use,Modular and Extendible package of deep-learning based CTR models.DeepFM,DeepInterestNetwork(DIN),DeepInterestEvolutionNetwork(DIEN),DeepCrossNetwork(DCN),AttentionalFactorizationMachine(AFM),Neural Factorization Machine(NFM),AutoInt,Deep Session Interest Network(DSIN)","url_abs":"http://arxiv.org/abs/1904.04447v1","url_pdf":"http://arxiv.org/pdf/1904.04447v1.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":"feature-generation-by-convolutional-neural","repo_url":"https://github.com/UlionTse/mlgb","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"feature-generation-by-convolutional-neural","repo_url":"https://github.com/shenweichen/DeepCTR","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"feature-generation-by-convolutional-neural","repo_url":"https://github.com/shenweichen/DeepCTR-PyTorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"feature-generation-by-convolutional-neural","repo_url":"https://github.com/xue-pai/FuxiCTR","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"feature-generation-by-convolutional-neural","repo_url":"https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5/models/rank/fgcnn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"paddle","reach":null},{"paper_slug":"feature-generation-by-convolutional-neural","repo_url":"https://github.com/chenjiyan2001/paddle-FGCNN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"paddle","reach":{"status":"ok"}}],"tasks":[{"task_slug":"click-through-rate-prediction","task_name":"Click-Through Rate Prediction"},{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"recommendation-systems","task_name":"Recommendation Systems"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/click-through-rate-prediction-on-avazu","task":"Click-Through Rate Prediction","dataset":"Avazu","model":"FGCNN+IPNN","rank_in_archive_order":9,"of":15,"metrics":{"AUC":"0.7883","LogLoss":"0.3746"},"uses_additional_data":false},{"leaderboard":"/sota/click-through-rate-prediction-on-huawei-app","task":"Click-Through Rate Prediction","dataset":"Huawei App Store","model":"FGCNN+IPNN","rank_in_archive_order":1,"of":1,"metrics":{"AUC":"0.9407","Log Loss":"0.1134"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}