/home_for_adser

some useful papers and blogs for people who are interested in online advertising

智能出价

[IJCAI2017, Alibaba]. Optimized Cost per Click in Taobao Display Advertising
[KDD2019, Alibaba]. Bid Optimization by Multivariable Control in Display Advertising
[AAMAS2020, ByteDance]. Optimized Cost per Mille in Feeds Advertising
[KDD2021, Alibaba]. A Unified Solution to Constrained Bidding in Online Display Advertising
[2021, Fly Adser]. RTB论文梳理及总结

排序策略

[ORSUM2019, Alibaba]. Optimal Delivery with Budget Constraint in E-Commerce Advertising
[KDD2020, LinkedIn]. Ads Allocation in Feed via Constrained Optimization
[2020, KuaiShou]. Cold Start on Online Advertising Platforms: Data-Driven Algorithms and Field Experiments
[2021, Fly Adser]. 多约束条件下的排序算法设计

重排算法

[IJCAJ2018, Alibaba]. Globally Optimized Mutual Influence Aware Ranking in E-Commerce Search
[SIGIR2018, Qingyao Ai]. Learning a Deep Listwise Context Model for Ranking Refinement
[RecSys2019, Alibaba]. Personalized Re-ranking for Recommendation
[CIKM2020, Alibaba]. EdgeRec-Recommender System on Edge in Mobile Taobao
[Artix2021, Alibaba]. Revisit Recommender System in the Permutation Prospective
[2022, Fly Adser]. 基于上下文感知的重排序算法梳理

决策艺术

[2021, Fly Adser]. 优化问题中的对偶理论
[2021, Fly Adser]. PID控制算法

CTR预估

[ICDM2010, Steffen Rendle]. Factorization Machines
[KDD2014, Facebook]. Practical Lessons from Predicting Clicks on Ads at Facebook
[RecSys2016]. Field-aware Factorization Machines for CTR Prediction
[DLRS2016, Geogle]. Wide & Deep Learning for Recommender Systems
[TOIS2016]. Product-based Neural Networks for User Response Prediction
[IJCAI2017, Huawei]. DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
[IJCAJ2017]. Attentional Factorization Machines-Learning the Weight of Feature Interactions via Attention Networks
[KDD2017, Geogle]. Deep & Cross Network for Ad Click Predictions
[KDD2018, Microsoft]. xDeepFM-Combining Explicit and Implicit Feature Interactions for Recommender Systems
[2022, Fly Adser]. 广告pCTR校准机制

CVR预估

[SIGIR2018, Alibaba]. Entire Space Multi-Task Model-An Effective Approach for Estimating Post-Click Conversion Rate

LTR预估

[ICML2005, Microsoft]. Learning to Rank using Gradient Descent
[Report2010, MSRA]. From RankNet to LambdaRank to LambdaMART-An overview
[2021, Fly Adser]. LTR预估：从慕名而来到一探深浅
[2022, Fly Adser]. LTR预估：从一探深浅到实战演练

Other MLs

[KDD2016, Tianqi Chen]. XGBoost: A Scalable Tree Boosting System