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Title: GitHub - FengNote/Ad-papers: Papers on Computational Advertising

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Description: Papers on Computational Advertising. Contribute to FengNote/Ad-papers development by creating an account on GitHub.

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Explore and Exploithttps://patch-diff.githubusercontent.com/FengNote/Ad-papers/tree/master/Explore%20and%20Exploit
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https://patch-diff.githubusercontent.com/FengNote/Ad-papers#计算广告论文学习资料业界分享
王喆http://wangzhe.website/about/
王喆的知乎https://www.zhihu.com/people/wang-zhe-58
王喆的主页http://wangzhe.website/about/
Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/CTR%20Prediction/Learning%20Piece-wise%20Linear%20Models%20from%20Large%20Scale%20Data%20for%20Ad%20Click%20Prediction.pdf
Deep Interest Network for Click-Through Rate Prediction.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/CTR%20Prediction/Deep%20Interest%20Network%20for%20Click-Through%20Rate%20Prediction.pdf
https://patch-diff.githubusercontent.com/FengNote/Ad-papers#目录
https://patch-diff.githubusercontent.com/FengNote/Ad-papers#allocation
Ad Serving Using a Compact Allocation Plan.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/Allocation/Ad%20Serving%20Using%20a%20Compact%20Allocation%20Plan.pdf
An Efficient Algorithm for Allocation of Guaranteed Display Advertising.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/Allocation/An%20Efficient%20Algorithm%20for%20Allocation%20of%20Guaranteed%20Display%20Advertising.pdf
https://patch-diff.githubusercontent.com/FengNote/Ad-papers#bidding-strategy
Combining Powers of Two Predictors in Optimizing Real-Time Bidding Strategy under Constrained Budget.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/Bidding%20Strategy/Combining%20Powers%20of%20Two%20Predictors%20in%20Optimizing%20Real-Time%20Bidding%20Strategy%20under%20Constrained%20Budget.pdf
Real-Time Bidding Algorithms for Performance-Based Display Ad Allocation.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/Bidding%20Strategy/Real-Time%20Bidding%20Algorithms%20for%20Performance-Based%20Display%20Ad%20Allocation.pdf
Real-Time Bidding by Reinforcement Learning in Display Advertising.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/Bidding%20Strategy/Real-Time%20Bidding%20by%20Reinforcement%20Learning%20in%20Display%20Advertising.pdf
Research Frontier of Real-Time Bidding based Display Advertising.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/Bidding%20Strategy/Research%20Frontier%20of%20Real-Time%20Bidding%20based%20Display%20Advertising.pdf
https://patch-diff.githubusercontent.com/FengNote/Ad-papers#budget-control
Budget Pacing for Targeted Online Advertisements at LinkedIn.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/Budget%20Control/Budget%20Pacing%20for%20Targeted%20Online%20Advertisements%20at%20LinkedIn.pdf
PID控制原理与控制算法.dochttps://github.com/wzhe06/Ad-papers/blob/master/Budget%20Control/PID%E6%8E%A7%E5%88%B6%E5%8E%9F%E7%90%86%E4%B8%8E%E6%8E%A7%E5%88%B6%E7%AE%97%E6%B3%95.doc
PID控制经典培训教程.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/Budget%20Control/PID%E6%8E%A7%E5%88%B6%E7%BB%8F%E5%85%B8%E5%9F%B9%E8%AE%AD%E6%95%99%E7%A8%8B.pdf
Predicting Traffic of Online Advertising in Real-time Bidding Systems from Perspective of Demand-Side Platforms.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/Budget%20Control/Predicting%20Traffic%20of%20Online%20Advertising%20in%20Real-time%20Bidding%20Systems%20from%20Perspective%20of%20Demand-Side%20Platforms.pdf
Real Time Bid Optimization with Smooth Budget Delivery in Online Advertising.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/Budget%20Control/Real%20Time%20Bid%20Optimization%20with%20Smooth%20Budget%20Delivery%20in%20Online%20Advertising.pdf
Smart Pacing for Effective Online Ad Campaign Optimization.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/Budget%20Control/Smart%20Pacing%20for%20Effective%20Online%20Ad%20Campaign%20Optimization.pdf
https://patch-diff.githubusercontent.com/FengNote/Ad-papers#computational-advertising-architect
Display Advertising with Real-Time Bidding (RTB) and Behavioural Targeting.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/Computational%20Advertising%20Architect/Display%20Advertising%20with%20Real-Time%20Bidding%20%28RTB%29%20and%20Behavioural%20Targeting.pdf
Parameter Server for Distributed Machine Learning.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/Computational%20Advertising%20Architect/Parameter%20Server%20for%20Distributed%20Machine%20Learning.pdf
Scaling Distributed Machine Learning with the Parameter Server.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/Computational%20Advertising%20Architect/Scaling%20Distributed%20Machine%20Learning%20with%20the%20Parameter%20Server.pdf
大数据下的广告排序技术及实践.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/Computational%20Advertising%20Architect/%E5%A4%A7%E6%95%B0%E6%8D%AE%E4%B8%8B%E7%9A%84%E5%B9%BF%E5%91%8A%E6%8E%92%E5%BA%8F%E6%8A%80%E6%9C%AF%E5%8F%8A%E5%AE%9E%E8%B7%B5.pdf
美团机器学习 吃喝玩乐中的算法问题.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/Computational%20Advertising%20Architect/%E7%BE%8E%E5%9B%A2%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%20%E5%90%83%E5%96%9D%E7%8E%A9%E4%B9%90%E4%B8%AD%E7%9A%84%E7%AE%97%E6%B3%95%E9%97%AE%E9%A2%98.pdf
https://patch-diff.githubusercontent.com/FengNote/Ad-papers#ctr-prediction
Ad Click Prediction a View from the Trenches.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/CTR%20Prediction/Ad%20Click%20Prediction%20a%20View%20from%20the%20Trenches.pdf
Adaptive Targeting for Online Advertisement.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/CTR%20Prediction/Adaptive%20Targeting%20for%20Online%20Advertisement.pdf
Deep Crossing- Web-Scale Modeling without Manually Crafted Combinatorial Features.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/CTR%20Prediction/Deep%20Crossing-%20Web-Scale%20Modeling%20without%20Manually%20Crafted%20Combinatorial%20Features.pdf
Deep Interest Network for Click-Through Rate Prediction.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/CTR%20Prediction/Deep%20Interest%20Network%20for%20Click-Through%20Rate%20Prediction.pdf
Deep Neural Networks for YouTube Recommendations.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/CTR%20Prediction/Deep%20Neural%20Networks%20for%20YouTube%20Recommendations.pdf
Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/CTR%20Prediction/Learning%20Piece-wise%20Linear%20Models%20from%20Large%20Scale%20Data%20for%20Ad%20Click%20Prediction.pdf
Logistic Regression in Rare Events Data.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/CTR%20Prediction/Logistic%20Regression%20in%20Rare%20Events%20Data.pdf
Practical Lessons from Predicting Clicks on Ads at Facebook.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/CTR%20Prediction/Practical%20Lessons%20from%20Predicting%20Clicks%20on%20Ads%20at%20Facebook.pdf
Wide & Deep Learning for Recommender Systems.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/CTR%20Prediction/Wide%20%26%20Deep%20Learning%20for%20Recommender%20Systems.pdf
https://patch-diff.githubusercontent.com/FengNote/Ad-papers#explore-and-exploit
A Contextual-Bandit Approach to Personalized News Article Recommendation(LinUCB).pdfhttps://github.com/wzhe06/Ad-papers/blob/master/Explore%20and%20Exploit/A%20Contextual-Bandit%20Approach%20to%20Personalized%20News%20Article%20Recommendation%28LinUCB%29.pdf
A Fast and Simple Algorithm for Contextual Bandits.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/Explore%20and%20Exploit/A%20Fast%20and%20Simple%20Algorithm%20for%20Contextual%20Bandits.pdf
An Empirical Evaluation of Thompson Sampling.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/Explore%20and%20Exploit/An%20Empirical%20Evaluation%20of%20Thompson%20Sampling.pdf
Analysis of Thompson Sampling for the Multi-armed Bandit Problem.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/Explore%20and%20Exploit/Analysis%20of%20Thompson%20Sampling%20for%20the%20Multi-armed%20Bandit%20Problem.pdf
Bandit Algorithms Continued- UCB1.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/Explore%20and%20Exploit/Bandit%20Algorithms%20Continued-%20UCB1.pdf
Bandit based Monte-Carlo Planning.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/Explore%20and%20Exploit/Bandit%20based%20Monte-Carlo%20Planning.pdf
Customer Acquisition via Display Advertising Using MultiArmed Bandit Experiments.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/Explore%20and%20Exploit/Customer%20Acquisition%20via%20Display%20Advertising%20Using%20MultiArmed%20Bandit%20Experiments.pdf
Dynamic Online Pricing with Incomplete Information Using Multi-Armed Bandit Experiments.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/Explore%20and%20Exploit/Dynamic%20Online%20Pricing%20with%20Incomplete%20Information%20Using%20Multi-Armed%20Bandit%20Experiments.pdf
EandE.pptxhttps://github.com/wzhe06/Ad-papers/blob/master/Explore%20and%20Exploit/EandE.pptx
Exploitation and Exploration in a Performance based Contextual Advertising System.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/Explore%20and%20Exploit/Exploitation%20and%20Exploration%20in%20a%20Performance%20based%20Contextual%20Advertising%20System.pdf
Exploration exploitation in Go UCT for Monte-Carlo Go.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/Explore%20and%20Exploit/Exploration%20exploitation%20in%20Go%20UCT%20for%20Monte-Carlo%20Go.pdf
Exploring compact reinforcement-learning representations with linear regression.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/Explore%20and%20Exploit/Exploring%20compact%20reinforcement-learning%20representations%20with%20linear%20regression.pdf
Finite-time Analysis of the Multiarmed Bandit Problem.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/Explore%20and%20Exploit/Finite-time%20Analysis%20of%20the%20Multiarmed%20Bandit%20Problem.pdf
Hierarchical Deep Reinforcement Learning- Integrating Temporal Abstraction and Intrinsic Motivation.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/Explore%20and%20Exploit/Hierarchical%20Deep%20Reinforcement%20Learning-%20Integrating%20Temporal%20Abstraction%20and%20Intrinsic%20Motivation.pdf
INCENTIVIZING EXPLORATION IN REINFORCEMENT LEARNING WITH DEEP PREDICTIVE MODELS.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/Explore%20and%20Exploit/INCENTIVIZING%20EXPLORATION%20IN%20REINFORCEMENT%20LEARNING%20WITH%20DEEP%20PREDICTIVE%20MODELS.pdf
Mastering the game of Go with deep neural networks and tree search.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/Explore%20and%20Exploit/Mastering%20the%20game%20of%20Go%20with%20deep%20neural%20networks%20and%20tree%20search.pdf
Multi-Armed Bandits Gittins Index and Its Calculation.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/Explore%20and%20Exploit/Multi-Armed%20Bandits%20Gittins%20Index%20and%20Its%20Calculation.pdf
On the Prior Sensitivity of Thompson Sampling.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/Explore%20and%20Exploit/On%20the%20Prior%20Sensitivity%20of%20Thompson%20Sampling.pdf
Provable Optimal Algorithms for Generalized Linear Contextual Bandits.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/Explore%20and%20Exploit/Provable%20Optimal%20Algorithms%20for%20Generalized%20Linear%20Contextual%20Bandits.pdf
Random Forest for the Contextual Bandit Problem.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/Explore%20and%20Exploit/Random%20Forest%20for%20the%20Contextual%20Bandit%20Problem.pdf
Thompson Sampling PPT.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/Explore%20and%20Exploit/Thompson%20Sampling%20PPT.pdf
UCT算法.dochttps://github.com/wzhe06/Ad-papers/blob/master/Explore%20and%20Exploit/UCT%E7%AE%97%E6%B3%95.doc
Unifying Count-Based Exploration and Intrinsic Motivation.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/Explore%20and%20Exploit/Unifying%20Count-Based%20Exploration%20and%20Intrinsic%20Motivation.pdf
Using Confidence Bounds for Exploitation-Exploration Trade-offs.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/Explore%20and%20Exploit/Using%20Confidence%20Bounds%20for%20Exploitation-Exploration%20Trade-offs.pdf
Variational Information Maximizing Exploration.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/Explore%20and%20Exploit/Variational%20Information%20Maximizing%20Exploration.pdf
基于UCT的围棋引擎的研究与实现.dochttps://github.com/wzhe06/Ad-papers/blob/master/Explore%20and%20Exploit/%E5%9F%BA%E4%BA%8EUCT%E7%9A%84%E5%9B%B4%E6%A3%8B%E5%BC%95%E6%93%8E%E7%9A%84%E7%A0%94%E7%A9%B6%E4%B8%8E%E5%AE%9E%E7%8E%B0.doc
对抗搜索、多臂老虎机问题、UCB算法.ppthttps://github.com/wzhe06/Ad-papers/blob/master/Explore%20and%20Exploit/%E5%AF%B9%E6%8A%97%E6%90%9C%E7%B4%A2%E3%80%81%E5%A4%9A%E8%87%82%E8%80%81%E8%99%8E%E6%9C%BA%E9%97%AE%E9%A2%98%E3%80%81UCB%E7%AE%97%E6%B3%95.ppt
https://patch-diff.githubusercontent.com/FengNote/Ad-papers#factorization-machines
Factorization Machines Rendle2010.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/Factorization%20Machines/Factorization%20Machines%20Rendle2010.pdf
Fast Context-aware Recommendations with Factorization Machines.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/Factorization%20Machines/Fast%20Context-aware%20Recommendations%20with%20Factorization%20Machines.pdf
fastFM- A Library for Factorization Machines.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/Factorization%20Machines/fastFM-%20A%20Library%20for%20Factorization%20Machines.pdf
FM PPT by CMU.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/Factorization%20Machines/FM%20PPT%20by%20CMU.pdf
libfm-1.42.manual.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/Factorization%20Machines/libfm-1.42.manual.pdf
Scaling Factorization Machines to Relational Data.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/Factorization%20Machines/Scaling%20Factorization%20Machines%20to%20Relational%20Data.pdf
https://patch-diff.githubusercontent.com/FengNote/Ad-papers#google-three-papers
Bigtable A Distributed Storage System for Structured Data.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/Google%20Three%20Papers/Bigtable%20A%20Distributed%20Storage%20System%20for%20Structured%20Data.pdf
MapReduce Simplified Data Processing on Large Clusters.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/Google%20Three%20Papers/MapReduce%20Simplified%20Data%20Processing%20on%20Large%20Clusters.pdf
The Google File System.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/Google%20Three%20Papers/The%20Google%20File%20System.pdf
https://patch-diff.githubusercontent.com/FengNote/Ad-papers#guaranteed-contracts-ads
A Dynamic Pricing Model for Unifying Programmatic Guarantee and Real-Time Bidding in Display Advertising.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/Guaranteed%20Contracts%20Ads/A%20Dynamic%20Pricing%20Model%20for%20Unifying%20Programmatic%20Guarantee%20and%20Real-Time%20Bidding%20in%20Display%20Advertising.pdf
Pricing Guaranteed Contracts in Online Display Advertising.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/Guaranteed%20Contracts%20Ads/Pricing%20Guaranteed%20Contracts%20in%20Online%20Display%20Advertising.pdf
Pricing Guidance in Ad Sale Negotiations The PrintAds Example.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/Guaranteed%20Contracts%20Ads/Pricing%20Guidance%20in%20Ad%20Sale%20Negotiations%20The%20PrintAds%20Example.pdf
Risk-Aware Dynamic Reserve Prices of Programmatic Guarantee in Display Advertising.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/Guaranteed%20Contracts%20Ads/Risk-Aware%20Dynamic%20Reserve%20Prices%20of%20Programmatic%20Guarantee%20in%20Display%20Advertising.pdf
Risk-Aware Revenue Maximization in Display Advertising.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/Guaranteed%20Contracts%20Ads/Risk-Aware%20Revenue%20Maximization%20in%20Display%20Advertising.pdf
https://patch-diff.githubusercontent.com/FengNote/Ad-papers#machine-learning-tutorial
Deep Learning Tutorial.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/Machine%20Learning%20Tutorial/Deep%20Learning%20Tutorial.pdf
Rules of Machine Learning- Best Practices for ML Engineering.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/Machine%20Learning%20Tutorial/Rules%20of%20Machine%20Learning-%20Best%20Practices%20for%20ML%20Engineering.pdf
关联规则基本算法及其应用.dochttps://github.com/wzhe06/Ad-papers/blob/master/Machine%20Learning%20Tutorial/%E5%85%B3%E8%81%94%E8%A7%84%E5%88%99%E5%9F%BA%E6%9C%AC%E7%AE%97%E6%B3%95%E5%8F%8A%E5%85%B6%E5%BA%94%E7%94%A8.doc
各种回归的概念学习.dochttps://github.com/wzhe06/Ad-papers/blob/master/Machine%20Learning%20Tutorial/%E5%90%84%E7%A7%8D%E5%9B%9E%E5%BD%92%E7%9A%84%E6%A6%82%E5%BF%B5%E5%AD%A6%E4%B9%A0.doc
广义线性模型.ppthttps://github.com/wzhe06/Ad-papers/blob/master/Machine%20Learning%20Tutorial/%E5%B9%BF%E4%B9%89%E7%BA%BF%E6%80%A7%E6%A8%A1%E5%9E%8B.ppt
机器学习总图.jpghttps://github.com/wzhe06/Ad-papers/blob/master/Machine%20Learning%20Tutorial/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E6%80%BB%E5%9B%BE.jpg
贝叶斯统计学(PPT).pdfhttps://github.com/wzhe06/Ad-papers/blob/master/Machine%20Learning%20Tutorial/%E8%B4%9D%E5%8F%B6%E6%96%AF%E7%BB%9F%E8%AE%A1%E5%AD%A6%28PPT%29.pdf
https://patch-diff.githubusercontent.com/FengNote/Ad-papers#optimization-method
A Survey on Algorithms of the Regularized Convex Optimization Problem.pptxhttps://github.com/wzhe06/Ad-papers/blob/master/Optimization%20Method/A%20Survey%20on%20Algorithms%20of%20the%20Regularized%20Convex%20Optimization%20Problem.pptx
Follow-the-Regularized-Leader and Mirror Descent- Equivalence Theorems and L1 Regularization.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/Optimization%20Method/Follow-the-Regularized-Leader%20and%20Mirror%20Descent-%20Equivalence%20Theorems%20and%20L1%20Regularization.pdf
Hogwild A Lock-Free Approach to Parallelizing Stochastic Gradient Descent.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/Optimization%20Method/Hogwild%20A%20Lock-Free%20Approach%20to%20Parallelizing%20Stochastic%20Gradient%20Descent.pdf
Parallelized Stochastic Gradient Descent.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/Optimization%20Method/Parallelized%20Stochastic%20Gradient%20Descent.pdf
在线最优化求解(Online Optimization)-冯扬.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/Optimization%20Method/%E5%9C%A8%E7%BA%BF%E6%9C%80%E4%BC%98%E5%8C%96%E6%B1%82%E8%A7%A3%28Online%20Optimization%29-%E5%86%AF%E6%89%AC.pdf
非线性规划.dochttps://github.com/wzhe06/Ad-papers/blob/master/Optimization%20Method/%E9%9D%9E%E7%BA%BF%E6%80%A7%E8%A7%84%E5%88%92.doc
https://patch-diff.githubusercontent.com/FengNote/Ad-papers#recommendation
基于BPR-MF算法的推荐系统设计.docxhttps://github.com/wzhe06/Ad-papers/blob/master/Recommendation/%E5%9F%BA%E4%BA%8EBPR-MF%E7%AE%97%E6%B3%95%E7%9A%84%E6%8E%A8%E8%8D%90%E7%B3%BB%E7%BB%9F%E8%AE%BE%E8%AE%A1.docx
微博推荐策略平台Eros.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/Recommendation/%E5%BE%AE%E5%8D%9A%E6%8E%A8%E8%8D%90%E7%AD%96%E7%95%A5%E5%B9%B3%E5%8F%B0Eros.pdf
https://patch-diff.githubusercontent.com/FengNote/Ad-papers#topic-model
Dirichlet Distribution, Dirichlet Process and Dirichlet Process Mixture(PPT).pdfhttps://github.com/wzhe06/Ad-papers/blob/master/Topic%20Model/Dirichlet%20Distribution%2C%20Dirichlet%20Process%20and%20Dirichlet%20Process%20Mixture%28PPT%29.pdf
LDA数学八卦.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/Topic%20Model/LDA%E6%95%B0%E5%AD%A6%E5%85%AB%E5%8D%A6.pdf
Parameter estimation for text analysis.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/Topic%20Model/Parameter%20estimation%20for%20text%20analysis.pdf
概率语言模型及其变形系列.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/Topic%20Model/%E6%A6%82%E7%8E%87%E8%AF%AD%E8%A8%80%E6%A8%A1%E5%9E%8B%E5%8F%8A%E5%85%B6%E5%8F%98%E5%BD%A2%E7%B3%BB%E5%88%97.pdf
理解共轭先验.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/Topic%20Model/%E7%90%86%E8%A7%A3%E5%85%B1%E8%BD%AD%E5%85%88%E9%AA%8C.pdf
https://patch-diff.githubusercontent.com/FengNote/Ad-papers#transfer-learning
A Survey on Transfer Learning.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/Transfer%20Learning/A%20Survey%20on%20Transfer%20Learning.pdf
Scalable Hands-Free Transfer Learning for Online Advertising.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/Transfer%20Learning/Scalable%20Hands-Free%20Transfer%20Learning%20for%20Online%20Advertising.pdf
https://patch-diff.githubusercontent.com/FengNote/Ad-papers#tree-model
Classification and Regression Trees.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/Tree%20Model/Classification%20and%20Regression%20Trees.pdf
Classification and Regression Trees.ppthttps://github.com/wzhe06/Ad-papers/blob/master/Tree%20Model/Classification%20and%20Regression%20Trees.ppt
Greedy Function Approximation A Gradient Boosting Machine.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/Tree%20Model/Greedy%20Function%20Approximation%20A%20Gradient%20Boosting%20Machine.pdf
Introduction to Boosted Trees.pdfhttps://github.com/wzhe06/Ad-papers/blob/master/Tree%20Model/Introduction%20to%20Boosted%20Trees.pdf
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