[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-83926-en":3,"doc-seo-83926-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},83926,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","CanniUplift Holistic Framework for Mitigating Seller and Incentive Cannibalization in E-commerce Uplift Modeling","Personalized incentive allocation is essential in e-commerce, where uplift modeling estimates Individual Treatment Effects (ITE). Traditional approaches underperform in multi-seller settings that violate SUTVA by causing two forms of cannibalization: seller-level shifts of spend across shops without platform growth and incentive-level distortions from organic conversions and alternative rewards. CanniUplift unifies Platform-level Global Alignment (PGA) with Redemption-based Decomposition Denoising (RDD) and a Treat-Attention mechanism, improving incrementality estimation and platform ΔGMV via experiments and online deployment.","CanniUplift: A Holistic Framework for Mitigating Seller and Incentive Cannibalization in E-commerce Uplift Modeling  \narXiv :2607 .05242v 1 [ cs .LG] 6 Jul 2026  \nZuwang He∗ [zuwanghe.hzw@gmail.com](zuwanghe.hzw@gmail.com)[ ](zuwanghe.hzw@gmail.com)[Taobao & Tmall Group of Alibaba](Taobao & Tmall Group of Alibaba)[ ](Taobao & Tmall Group of Alibaba)Beijing, China  \nHanyu Gao  \n[gaohanyu.ghy@alibaba-inc.com](gaohanyu.ghy@alibaba-inc.com)[ ](gaohanyu.ghy@alibaba-inc.com)[Taobao & Tmall Group of Alibaba](Taobao & Tmall Group of Alibaba)[ ](Taobao & Tmall Group of Alibaba)Beijing, China  \nXiangda Yan  \n[xiangda.yxd@alibaba-inc.com](xiangda.yxd@alibaba-inc.com)[ ](xiangda.yxd@alibaba-inc.com)[Taobao & Tmall Group of Alibaba](Taobao & Tmall Group of Alibaba)[ ](Taobao & Tmall Group of Alibaba)Hangzhou, Zhejiang, China  \nShihao Shu∗ [shushihao.ssh@alibaba-inc.com](shushihao.ssh@alibaba-inc.com)[ ](shushihao.ssh@alibaba-inc.com)[Taobao & Tmall Group of Alibaba](Taobao & Tmall Group of Alibaba)[ ](Taobao & Tmall Group of Alibaba)Beijing, China  \nZiliang Zhang  \n[zhangziliang.zzl@alibaba-inc.com](zhangziliang.zzl@alibaba-inc.com)[ ](zhangziliang.zzl@alibaba-inc.com)[Taobao & Tmall Group of Alibaba](Taobao & Tmall Group of Alibaba)[ ](Taobao & Tmall Group of Alibaba)Beijing, China  \nBuyu Gao  \n[gaobuyu.gby@alibaba-inc.com](gaobuyu.gby@alibaba-inc.com)[ ](gaobuyu.gby@alibaba-inc.com)[Taobao & Tmall Group of Alibaba](Taobao & Tmall Group of Alibaba)[ ](Taobao & Tmall Group of Alibaba)Hangzhou, Zhejiang, China  \nYuli Qu∗ [quyuli.qyl@alibaba-inc.com](quyuli.qyl@alibaba-inc.com)[ ](quyuli.qyl@alibaba-inc.com)[Taobao & Tmall Group of Alibaba](Taobao & Tmall Group of Alibaba)[ ](Taobao & Tmall Group of Alibaba)Hangzhou, Zhejiang, China  \nDiwei Chen  \n[chendiwei.cdw@alibaba-inc.com](chendiwei.cdw@alibaba-inc.com)[ ](chendiwei.cdw@alibaba-inc.com)[Taobao & Tmall Group of Alibaba](Taobao & Tmall Group of Alibaba)[ ](Taobao & Tmall Group of Alibaba)Hangzhou, Zhejiang, China  \nTanchao Zhu  \n[tanchao.zhutc@alibaba-inc.com](tanchao.zhutc@alibaba-inc.com)[ ](tanchao.zhutc@alibaba-inc.com)[Taobao & Tmall Group of Alibaba](Taobao & Tmall Group of Alibaba)[ ](Taobao & Tmall Group of Alibaba)Hangzhou, Zhejiang, China  \nYumeng Li† [lym174806@alibaba-inc.com](lym174806@alibaba-inc.com)[ ](lym174806@alibaba-inc.com)[Taobao & Tmall Group of Alibaba](Taobao & Tmall Group of Alibaba)[ ](Taobao & Tmall Group of Alibaba)Beijing, China  \nJunxiong Zhu  \n[xike.zjx@alibaba-inc.com](xike.zjx@alibaba-inc.com)[ ](xike.zjx@alibaba-inc.com)Taobao & Tmall Group of Alibaba Hangzhou, Zhejiang, China  \nAbstract  \nPersonalized incentive allocation is vital for e-commerce, where uplift modeling is the standard for estimating Individual Treatment Effects (ITE) . However, traditional models often fail in complex multiseller environments with violations ofthe Stable Unit Treatment Value Assumption (SUTVA) . We identify two critical challenges: Seller-level Cannibalization, where incentives shift expenditure between shops without growing the platform, and Incentive-level Cannibalization, where organic conversions or alternative rewards introduce significant noise into incrementality estimation. In this paper, we propose CanniUplift, a unified framework to mitigate these dual-source cannibalization effects. Specifically, we design Platform-level Global Alignment (PGA) to capture cross-shop substitution through global GMV consistency constraints. To tackle incentive-driven noise, we introduce Redemption-based Decomposition Denoising (RDD), which uses redemption behavior to decompose treated outcomes and reduce attribution noise within an entire-space framework. Furthermore, a Treat-Attention mechanism is designed to model intricate interactions between users’historical behaviors and current treatment options. Extensive experiments on both synthetic and large-scale industrial datasets demonstrate that CanniUplift significantly outperforms state-ofthe-art baselines. Ablation studies con","cbCaih4fFdLmoI3s","https://ap.wps.com/l/cbCaih4fFdLmoI3s","pdf",2008294,1,12,"English","en",105,"# Introduction\n# Abstract\n# CCS Concepts\n# Keywords\n# Method and Framework (PGA, RDD, Treat-Attention)\n# Experiments and Results\n# Ablation Studies\n# Deployment and Impact","[{\"question\":\"What problems does CanniUplift address in e-commerce uplift modeling?\",\"answer\":\"It addresses seller-level cannibalization, where incentives move spend between shops without growing platform GMV, and incentive-level cannibalization, where organic conversions or other rewards add noise to incrementality estimation.\"},{\"question\":\"How does Platform-level Global Alignment (PGA) reduce seller-level cannibalization?\",\"answer\":\"PGA captures cross-shop substitution using global GMV consistency constraints, aligning treated outcomes across the platform level.\"},{\"question\":\"How does Redemption-based Decomposition Denoising (RDD) improve incentive-level noise handling?\",\"answer\":\"RDD uses redemption behavior to decompose treated outcomes, reducing attribution noise within an entire-space framework and improving incrementality 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