业务策略算法

业务篇

1. [广告策略算法系列一]：前言
2. [广告策略算法系列二]：预算分配
3. [广告策略算法系列三]：广告创意优化
4. [广告策略算法系列四]：新广告冷启动优化
5. [广告策略算法系列五]：成本控制策略
6. [广告策略算法系列六]：匀速投放
7. [广告策略算法系列七]：预估校准机制
8. [广告策略算法系列八]：多约束条件下的出价优化
9. [广告策略算法系列九]：多约束条件下的排序公式优化
10. [广告策略算法系列十]：混排策略和算法
11. [广告策略算法系列十一]：联盟RTB策略

数据和算法篇

1. [广告策略算法系列十二]：浅谈博弈论与经济学的关系
2. [广告策略算法系列十三]：优化问题中的对偶理论
3. [广告策略算法系列十四]：常用预估模型及TF实现
4. [广告策略算法系列十五]：LTR预估
5. [广告策略算法系列十六]：基于上下文感知的重排序算法
6. [广告策略算法系列十七]：强化学习基础
7. [广告策略算法系列十八]：Spark编程

论文分享

智能出价

[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

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

竞价环境预估

[KDD2014]. Optimal Real-Time Bidding for Display Advertising
[KDD2015]. Bid Landscape Forecasting in Online Ad Exchange Marketplace
[KDD2015]. Predicting Winning Price in Real Time Bidding with Censored Data
[KDD2016]. User Response Learning for Directly Optimizing Campaign Performance in Display Advertising
[KDD2016]. Functional Bid Landscape Forecasting for Display Advertising
[KDD2017]. A Gamma-Based Regression for Winning Price Estimation in Real-Time Bidding Advertising
[KDD2018]. Bidding Machine Learning to Bid for Directly Optimizing Profits in Display Advertising
[KDD2019]. Deep Landscape Forecasting for Real-time Bidding Advertising

广告创意优化

[April2021, Alibaba]. A Hybrid Bandit Model with Visual Priors for Creative Ranking in Display Advertising
[March2021, Alibaba]. Efficient Optimal Selection for Composited Advertising Creatives with Tree Structure
[April2021, Alibaba]. Automated Creative Optimization for E-Commerce Advertising
[SIGIR2022, Alibaba]. Joint Optimization of Ad Ranking and Creative Selection [NAACL2022, Alibaba]. CREATER: CTR-driven Advertising Text Generation with Controlled Pre-Training and Contrastive Fine-Tuning
[SIGIR2022, Alibaba]. Towards Personalized Bundle Creative Generation with Contrastive Non-Autoregressive Decoding
[WeChat2022, ByteDance]. 广告素材优选算法在内容营销中的应用实践

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

校准算法

[KDD2020, Alibaba]. Calibrating User Response Predictions in Online Advertising
[WWW2020, Tencent]. A Simple and Empirically Strong Method for Reliable Probabilistic Predictions
[WWW2022, Alibaba]. MBCT Tree-Based Feature-Aware Binning for Individual Uncertainty Calibration

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

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

Other MLs

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