{"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/flen-leveraging-field-for-scalable-ctr","title":"FLEN: Leveraging Field for Scalable CTR Prediction","arxiv_id":"1911.04690","date":"2019-11-12","proceeding":null,"authors":["Wenqiang Chen","Lizhang Zhan","Yuanlong Ci","Minghua Yang","Chen Lin","Dugang Liu"],"abstract":"Click-Through Rate (CTR) prediction has been an indispensable component for many industrial applications, such as recommendation systems and online advertising. CTR prediction systems are usually based on multi-field categorical features, i.e., every feature is categorical and belongs to one and only one field. Modeling feature conjunctions is crucial for CTR prediction accuracy. However, it requires a massive number of parameters to explicitly model all feature conjunctions, which is not scalable for real-world production systems. In this paper, we describe a novel Field-Leveraged Embedding Network (FLEN) which has been deployed in the commercial recommender system in Meitu and serves the main traffic. FLEN devises a field-wise bi-interaction pooling technique. By suitably exploiting field information, the field-wise bi-interaction pooling captures both inter-field and intra-field feature conjunctions with a small number of model parameters and an acceptable time complexity for industrial applications. We show that a variety of state-of-the-art CTR models can be expressed under this technique. Furthermore, we develop Dicefactor: a dropout technique to prevent independent latent features from co-adapting. Extensive experiments, including offline evaluations and online A/B testing on real production systems, demonstrate the effectiveness and efficiency of FLEN against the state-of-the-arts. Notably, FLEN has obtained 5.19% improvement on CTR with 1/6 of memory usage and computation time, compared to last version (i.e. NFM).","url_abs":"https://arxiv.org/abs/1911.04690v4","url_pdf":"https://arxiv.org/pdf/1911.04690v4.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":"flen-leveraging-field-for-scalable-ctr","repo_url":"https://github.com/aimetrics/jarvis","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"flen-leveraging-field-for-scalable-ctr","repo_url":"https://github.com/LinJayan/FLEN-Paddle","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"paddle","reach":null},{"paper_slug":"flen-leveraging-field-for-scalable-ctr","repo_url":"https://github.com/PaddlePaddle/PaddleRec","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"paddle","reach":null},{"paper_slug":"flen-leveraging-field-for-scalable-ctr","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":"flen-leveraging-field-for-scalable-ctr","repo_url":"https://github.com/shenweichen/DeepCTR","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"flen-leveraging-field-for-scalable-ctr","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":"flen-leveraging-field-for-scalable-ctr","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"}}],"tasks":[{"task_slug":"click-through-rate-prediction","task_name":"Click-Through Rate Prediction"},{"task_slug":"prediction","task_name":"Prediction"},{"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":"FLEN","rank_in_archive_order":15,"of":15,"metrics":{"AUC":"0.75"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}