CTR Prediction 论文、个人学习笔记分享

动态更新学习中实现或者阅读过的计算广告相关论文、学习资料和业界分享，作为自己学习的总结，也希望能为计算广告相关行业的同学带来便利。 同时欢迎对CTR Prediction感兴趣的同学与我(杜博亚)讨论相关问题，我的联系方式如下：

• Email: duboyabz@163.com
• 知乎私信: 杜博亚的知乎
• 个人博客: https://blog.csdn.net/dby_freedom?t=1

其中，个人博客收录了本人关于CTR的理论、实践总结，欢迎访问、关注~

目录

Optimization Method

Online Optimization，Parallel SGD，FTRL等优化方法，实用并且能够给出直观解释的文章

• Google Vizier A Service for Black-Box Optimization.pdf Google的深度学习自动调参框架Vizier
• 在线最优化求解(Online Optimization)-冯扬.pdf 非常推荐冯扬的这个教程，把在线优化问题讲的非常透
• Hogwild A Lock-Free Approach to Parallelizing Stochastic Gradient Descent.pdf
• Parallelized Stochastic Gradient Descent.pdf
• A Survey on Algorithms of the Regularized Convex Optimization Problem.pptx
• Follow-the-Regularized-Leader and Mirror Descent- Equivalence Theorems and L1 Regularization.pdf
• A Review of Bayesian Optimization.pdf
• Taking the Human Out of the Loop- A Review of Bayesian Optimization.pdf
• 非线性规划.doc

Classic CTR Prediction

• [LR] Predicting Clicks - Estimating the Click-Through Rate for New Ads (Microsoft 2007)
  
• [FFM] Field-aware Factorization Machines for CTR Prediction (Criteo 2016)
  
• [GBDT+LR] Practical Lessons from Predicting Clicks on Ads at Facebook (Facebook 2014)
  
• [PS-PLM] Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction (Alibaba 2017)
  
• [FTRL] Ad Click Prediction a View from the Trenches (Google 2013)
  
• [FM] Fast Context-aware Recommendations with Factorization Machines (UKON 2011)
  

Deep Learning CTR Prediction

• [DCN] Deep & Cross Network for Ad Click Predictions (Stanford 2017)
  
• [Deep Crossing] Deep Crossing - Web-Scale Modeling without Manually Crafted Combinatorial Features (Microsoft 2016)
  
• [PNN] Product-based Neural Networks for User Response Prediction (SJTU 2016)
  
• [DIN] Deep Interest Network for Click-Through Rate Prediction (Alibaba 2018)
  
• [ESMM] Entire Space Multi-Task Model - An Effective Approach for Estimating Post-Click Conversion Rate (Alibaba 2018)
  
• [Wide & Deep] Wide & Deep Learning for Recommender Systems (Google 2016)
  
• [xDeepFM] xDeepFM - Combining Explicit and Implicit Feature Interactions for Recommender Systems (USTC 2018)
  
• [Image CTR] Image Matters - Visually modeling user behaviors using Advanced Model Server (Alibaba 2018)
  
• [AFM] Attentional Factorization Machines - Learning the Weight of Feature Interactions via Attention Networks (ZJU 2017)
  
• [DIEN] Deep Interest Evolution Network for Click-Through Rate Prediction (Alibaba 2019)
  
• [DSSM] Learning Deep Structured Semantic Models for Web Search using Clickthrough Data (UIUC 2013)
  
• [FNN] Deep Learning over Multi-field Categorical Data (UCL 2016)
  
• [DeepFM] A Factorization-Machine based Neural Network for CTR Prediction (HIT-Huawei 2017)
  
• [NFM] Neural Factorization Machines for Sparse Predictive Analytics (NUS 2017)
  

Embedding

• [Negative Sampling] Word2vec Explained Negative-Sampling Word-Embedding Method (2014)
  
• [SDNE] Structural Deep Network Embedding (THU 2016)
  
• [Item2Vec] Item2Vec-Neural Item Embedding for Collaborative Filtering (Microsoft 2016)
  
• [Word2Vec] Distributed Representations of Words and Phrases and their Compositionality (Google 2013)
  
• [Word2Vec] Word2vec Parameter Learning Explained (UMich 2016)
  
• [Node2vec] Node2vec - Scalable Feature Learning for Networks (Stanford 2016)
  
• [Graph Embedding] DeepWalk- Online Learning of Social Representations (SBU 2014)
  
• [Airbnb Embedding] Real-time Personalization using Embeddings for Search Ranking at Airbnb (Airbnb 2018)
  
• [Alibaba Embedding] Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba (Alibaba 2018)
  
• [Word2Vec] Efficient Estimation of Word Representations in Vector Space (Google 2013)
  
• [LINE] LINE - Large-scale Information Network Embedding (MSRA 2015)
  

Tree Model

树模型和基于树模型的boosting模型，树模型的效果在大部分问题上非常好，在CTR，CVR预估及特征工程方面的应用非常广

• Introduction to Boosted Trees
  
• Classification and Regression Trees
  
• Greedy Function Approximation A Gradient Boosting Machine
  
• Classification and Regression Trees
  

Computational Advertising Architect

广告系统的架构问题

• [TensorFlow Whitepaper]TensorFlow- Large-Scale Machine Learning on Heterogeneous Distributed Systems
  
• 大数据下的广告排序技术及实践
  
• 美团机器学习 吃喝玩乐中的算法问题
  
• [Parameter Server]Scaling Distributed Machine Learning with the Parameter Server
  
• Display Advertising with Real-Time Bidding (RTB) and Behavioural Targeting
  
• A Comparison of Distributed Machine Learning Platforms
  
• Efficient Query Evaluation using a Two-Level Retrieval Process
  
• [TensorFlow Whitepaper]TensorFlow- A System for Large-Scale Machine Learning
  
• [Parameter Server]Parameter Server for Distributed Machine Learning
  
• Overlapping Experiment Infrastructure More, Better, Faster Experimentation
  

参考文献

[1] https://github.com/wzhe06/Ad-papers/blob/master/README.md [2] https://github.com/duboya/ML_CIA