ML papers

Allocation

1. Ad Serving Using a Compact Allocation Plan.pdf
2. An Efficient Algorithm for Allocation of Guaranteed Display Advertising.pdf

Bidding Strategy

1. Bid-aware Gradient Descent for Unbiased Learning with Censored Data in Display Advertising.pdf
2. Budget Constrained Bidding by Model-free Reinforcement Learning in Display Advertising.pdf
3. Combining Powers of Two Predictors in Optimizing Real-Time Bidding Strategy under Constrained Budget.pdf
4. Deep Reinforcement Learning for Sponsored Search Real-time Bidding.pdf
5. Optimized Cost per Click in Taobao Display Advertising.pdf
6. Real-Time Bidding Algorithms for Performance-Based Display Ad Allocation.pdf
7. Real-Time Bidding by Reinforcement Learning in Display Advertising.pdf
8. Real-Time Bidding with Multi-Agent Reinforcement Learning in Display Advertising.pdf
9. Research Frontier of Real-Time Bidding based Display Advertising.pdf

Budget Control

1. Budget Pacing for Targeted Online Advertisements at LinkedIn.pdf
2. PID控制原理与控制算法.doc
3. PID控制经典培训教程.pdf
4. Predicting Traffic of Online Advertising in Real-time Bidding Systems from Perspective of Demand-Side Platforms.pdf
5. Real Time Bid Optimization with Smooth Budget Delivery in Online Advertising.pdf
6. Smart Pacing for Effective Online Ad Campaign Optimization.pdf
7. 广告系统中的智能预算控制策略.pdf

Classic CTR Prediction

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

Classic Recommender System

1. [Bilinear] Personalized Recommendation on Dynamic Content Using Predictive Bilinear Models (Yahoo 2009).pdf
2. [CF] Amazon Recommendations Item-to-Item Collaborative Filtering (Amazon 2003).pdf
3. [Earliest CF] Using Collaborative Filtering to Weave an Information Tapestry (PARC 1992).pdf
4. [ItemCF] Item-Based Collaborative Filtering Recommendation Algorithms (UMN 2001).pdf
5. [MF] Matrix Factorization Techniques for Recommender Systems (Yahoo 2009).pdf
6. [Recsys Intro slides] Recommender Systems An introduction (DJannach 2014).pdf
7. [Recsys Intro] Recommender Systems Handbook (FRicci 2011).pdf

Computational Advertising Architect

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

Deep Learning CTR Prediction

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

Deep Learning Recommender System

1. [AFM] Attentional Factorization Machines - Learning the Weight of Feature Interactions via Attention Networks (ZJU 2017).pdf
2. [CDL] Collaborative Deep Learning for Recommender Systems (HKUST, 2015).pdf
3. [DCN] Deep & Cross Network for Ad Click Predictions (Stanford 2017).pdf
4. [Deep Crossing] Deep Crossing - Web-Scale Modeling without Manually Crafted Combinatorial Features (Microsoft 2016).pdf
5. [DeepFM] A Factorization-Machine based Neural Network for CTR Prediction (HIT-Huawei 2017).pdf
6. [DIEN] Deep Interest Evolution Network for Click-Through Rate Prediction (Alibaba 2019).pdf
7. [DIN] Deep Interest Network for Click-Through Rate Prediction (Alibaba 2018).pdf
8. [DL Recsys Intro] Deep Learning based Recommender System- A Survey and New Perspectives (UNSW 2018).pdf
9. [DSSM in Recsys] A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems (Microsoft 2015).pdf
10. [DSSM] Learning Deep Structured Semantic Models for Web Search using Clickthrough Data (UIUC 2013).pdf
11. [ESMM] Entire Space Multi-Task Model - An Effective Approach for Estimating Post-Click Conversion Rate (Alibaba 2018).pdf
12. [FNN] Deep Learning over Multi-field Categorical Data (UCL 2016).pdf
13. [Image CTR] Image Matters - Visually modeling user behaviors using Advanced Model Server (Alibaba 2018).pdf
14. [Latent Cross] Latent Cross- Making Use of Context in Recurrent Recommender Systems (Google 2018).pdf
15. [NCF] Neural Collaborative Filtering (NUS 2017).pdf
16. [NFM] Neural Factorization Machines for Sparse Predictive Analytics (NUS 2017).pdf
17. [PNN] Product-based Neural Networks for User Response Prediction (SJTU 2016).pdf
18. [Wide&Deep] Wide & Deep Learning for Recommender Systems (Google 2016).pdf
19. [xDeepFM] xDeepFM - Combining Explicit and Implicit Feature Interactions for Recommender Systems (USTC 2018).pdf

Distributed System

1. [Spark] Resilient Distributed Datasets A Fault-Tolerant Abstraction for In-Memory Cluster Computing.pdf

Embedding

1. [Airbnb Embedding] Real-time Personalization using Embeddings for Search Ranking at Airbnb (Airbnb 2018).pdf
2. [Alibaba Embedding] Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba (Alibaba 2018).pdf
3. [Graph Embedding] DeepWalk- Online Learning of Social Representations (SBU 2014).pdf
4. [Item2Vec] Item2Vec-Neural Item Embedding for Collaborative Filtering (Microsoft 2016).pdf
5. [LINE] LINE - Large-scale Information Network Embedding (MSRA 2015).pdf
6. [LSH] Locality-Sensitive Hashing for Finding Nearest Neighbors (IEEE 2008).pdf
7. [Negative Sampling] Word2vec Explained Negative-Sampling Word-Embedding Method (2014).pdf
8. [Node2vec] Node2vec - Scalable Feature Learning for Networks (Stanford 2016).pdf
9. [SDNE] Structural Deep Network Embedding (THU 2016).pdf
10. [Word2Vec] Distributed Representations of Words and Phrases and their Compositionality (Google 2013).pdf
11. [Word2Vec] Efficient Estimation of Word Representations in Vector Space (Google 2013).pdf
12. [Word2Vec] Word2vec Parameter Learning Explained (UMich 2016).pdf

Evaluation

1. [Bootstrapped Replay] Improving offline evaluation of contextual bandit algorithms via bootstrapping techniques (Ulille 2014).pdf
2. [Classic Metrics] A Survey of Accuracy Evaluation Metrics of Recommendation Tasks (Microsoft 2009).pdf
3. [EE Evaluation Intro] Offline Evaluation and Optimization for Interactive Systems (Microsoft 2015).pdf
4. [InterLeaving] Large-Scale Validation and Analysis of Interleaved Search Evaluation (Yahoo 2012).pdf
5. [Replay] Unbiased Offline Evaluation of Contextual-bandit-based News Article Recommendation Algorithms (Yahoo 2012).pdf

Exploration and Exploitation

1. A Contextual-Bandit Approach to Personalized News Article Recommendation(LinUCB).pdf
2. A Fast and Simple Algorithm for Contextual Bandits.pdf
3. An Empirical Evaluation of Thompson Sampling.pdf
4. Analysis of Thompson Sampling for the Multi-armed Bandit Problem.pdf
5. Bandit Algorithms Continued- UCB1.pdf
6. Bandit based Monte-Carlo Planning.pdf
7. Customer Acquisition via Display Advertising Using MultiArmed Bandit Experiments.pdf
8. Dynamic Online Pricing with Incomplete Information Using Multi-Armed Bandit Experiments.pdf
9. Exploitation and Exploration in a Performance based Contextual Advertising System.pdf
10. Exploration and Exploitation Problem by Wang Zhe.pptx
11. Exploration exploitation in Go UCT for Monte-Carlo Go.pdf
12. Exploring compact reinforcement-learning representations with linear regression.pdf
13. Finite-time Analysis of the Multiarmed Bandit Problem.pdf
14. Hierarchical Deep Reinforcement Learning- Integrating Temporal Abstraction and Intrinsic Motivation.pdf
15. Incentivizting Exploration in Reinforcement Learning with Deep Predictive Models.pdf
16. Mastering the game of Go with deep neural networks and tree search.pdf
17. Random Forest for the Contextual Bandit Problem.pdf
18. Thompson Sampling PPT.pdf
19. Unifying Count-Based Exploration and Intrinsic Motivation.pdf
20. Using Confidence Bounds for Exploitation-Exploration Trade-offs.pdf
21. [EE in Ads] Customer Acquisition via Display Advertising Using MultiArmed Bandit Experiments (UMich 2015).pdf
22. [EE in Ads] Exploitation and Exploration in a Performance based Contextual Advertising System (Yahoo 2010).pdf
23. [EE in AlphaGo]Mastering the game of Go with deep neural networks and tree search (Deepmind 2016).pdf
24. [EE Intro] Exploration and Exploitation Problem Introduction by Wang Zhe (Hulu 2017).pdf
25. [LinUCB] A Contextual-Bandit Approach to Personalized News Article Recommendation (Yahoo 2010).pdf
26. [RF in MAB]Random Forest for the Contextual Bandit Problem (Orange 2016).pdf
27. [Spotify] Explore, Exploit, and Explain- Personalizing Explainable Recommendations with Bandits (Spotify 2018).pdf
28. [Thompson Sampling] An Empirical Evaluation of Thompson Sampling (Yahoo 2011).pdf
29. [TS Intro] Thompson Sampling Slides (Berkeley 2010).pdf
30. [UCB1] Bandit Algorithms Continued - UCB1 (Noel Welsh 2010).pdf
31. [UCT] Exploration exploitation in Go UCT for Monte-Carlo Go (UPSUD 2016).pdf
32. 对抗搜索、多臂老虎机问题、UCB算法.ppt
33. 广告系统中的探索与利用算法.pdf

Factorization Machines

1. Factorization Machines Rendle2010.pdf
2. fastFM- A Library for Factorization Machines.pdf
3. FM PPT by CMU.pdf
4. libfm-1.42.manual.pdf
5. Scaling Factorization Machines to Relational Data.pdf

Famous Machine Learning Papers

1. [CNN] ImageNet Classification with Deep Convolutional Neural Networks (UofT 2012).pdf
2. [RNN] Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation (UofM 2014).pdf

Google Three Papers

1. Bigtable A Distributed Storage System for Structured Data.pdf
2. MapReduce Simplified Data Processing on Large Clusters.pdf
3. The Google File System.pdf

Guaranteed Contracts Ads

1. A Dynamic Pricing Model for Unifying Programmatic Guarantee and Real-Time Bidding in Display Advertising.pdf
2. Pricing Guaranteed Contracts in Online Display Advertising.pdf
3. Pricing Guidance in Ad Sale Negotiations The PrintAds Example.pdf
4. Risk-Aware Dynamic Reserve Prices of Programmatic Guarantee in Display Advertising.pdf
5. Risk-Aware Revenue Maximization in Display Advertising.pdf

Industry Recommender System

1. [Airbnb] Applying Deep Learning To Airbnb Search (Airbnb 2018).pdf
2. [Airbnb] Search Ranking and Personalization at Airbnb Slides (Airbnb 2018).pdf
3. [Baidu slides] DNN in Baidu Ads (Baidu 2017).pdf
4. [Netflix] The Netflix Recommender System- Algorithms, Business Value, and Innovation (Netflix 2015).pdf
5. [Pinterest] Graph Convolutional Neural Networks for Web-Scale Recommender Systems (Pinterest 2018).pdf
6. [Pinterest] Personalized content blending In the Pinterest home feed (Pinterest 2016).pdf
7. [Quora] Building a Machine Learning Platform at Quora (Quora 2016).pdf
8. [Youtube] Deep Neural Networks for YouTube Recommendations (Youtube 2016).pdf

Machine Learning Tutorial

1. An introduction to ROC analysis.pdf
2. Deep Learning Tutorial.pdf
3. Efficient Estimation of Word Representations in Vector Space.pdf
4. Rules of Machine Learning- Best Practices for ML Engineering.pdf
5. 关联规则基本算法及其应用.doc
6. 各种回归的概念学习.doc
7. 广义线性模型.ppt
8. 机器学习总图.jpg
9. 贝叶斯统计学(PPT).pdf

Optimization Method

1. A Review of Bayesian Optimization.pdf
2. A Survey on Algorithms of the Regularized Convex Optimization Problem.pptx
3. Follow-the-Regularized-Leader and Mirror Descent- Equivalence Theorems and L1 Regularization.pdf
4. Google Vizier A Service for Black-Box Optimization.pdf
5. Hogwild A Lock-Free Approach to Parallelizing Stochastic Gradient Descent.pdf
6. Parallelized Stochastic Gradient Descent.pdf
7. Taking the Human Out of the Loop- A Review of Bayesian Optimization.pdf
8. 在线最优化求解(Online Optimization)-冯扬.pdf
9. 非线性规划.doc

Reinforcement Learning in Reco

1. A survey of active learning in collaborative filtering recommender systems (POLIMI 2016).pdf
2. Active Learning in Collaborative Filtering Recommender Systems(UNIBZ 2014).pdf
3. DRN- A Deep Reinforcement Learning Framework for News Recommendation (MSRA 2018).pdf
4. Exploration in Interactive Personalized Music Recommendation- A Reinforcement Learning Approach (NUS 2013).pdf

Topic Model

1. Dirichlet Distribution, Dirichlet Process and Dirichlet Process Mixture(PPT).pdf
2. Distributed Representations of Words and Phrases and their Compositionality.pdf
3. LDA数学八卦.pdf
4. Parameter estimation for text analysis.pdf
5. 概率语言模型及其变形系列.pdf
6. 理解共轭先验.pdf

Transfer Learning

1. A Survey on Transfer Learning.pdf
2. Scalable Hands-Free Transfer Learning for Online Advertising.pdf
3. [Multi-Task]An Overview of Multi-Task Learning in Deep Neural Networks.pdf

Tree Model

1. Classification and Regression Trees.pdf
2. Classification and Regression Trees.ppt
3. Greedy Function Approximation A Gradient Boosting Machine.pdf
4. Introduction to Boosted Trees.pdf