【论文整理】推荐算法论文、学习资料x

　【论文整理】推荐算法论文、学习资料 推荐算法论文、学习资料 • New!

　[Airbnb Embedding] Real-time Personalization using Embeddings for Search Ranking at Airbnb (Airbnb 2018)

　2018 KDD best paper, Airbnb 基于 embeddding 构建的实时搜索推荐系统 • New!

　[DIEN] Deep Interest Evolution Network for Click-Through Rate Prediction (Alibaba 2019)

　阿里提出的深度兴趣网络（Deep Interest Network）最新改进 DIEN 其他相关资源 其他相关资源 • 张伟楠的 RTB Papers 列表

　• 基于 Spark MLlib 的 CTR 预估模型(LR, FM, RF, GBDT, NN, PNN)

　• 推荐系统相关论文和资源列表

　• Honglei Zhang 的推荐系统论文列表 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

　• 在线最优化求解(Online Optimization)-冯扬

　• Hogwild A Lock-Free Approach to Parallelizing Stochastic Gradient Descent

　• Parallelized Stochastic Gradient Descent

　• A Survey on Algorithms of the Regularized Convex Optimization Problem

　• Follow-the-Regularized-Leader and Mirror Descent- Equivalence Theorems and L1 Regularization

　• A Review of Bayesian Optimization

　• Taking the Human Out of the Loop- A Review of Bayesian Optimization

　• 非线性规划

　Topic Model 话题模型相关文章，PLSA，LDA，进行广告 Context 特征提取，创意优化经常会用到 Topic Model • 概率语言模型及其变形系列

　• Parameter estimation for text analysis

　• LDA 数学八卦

　• Distributed Representations of Words and Phrases and their Compositionality

　• Dirichlet Distribution, Dirichlet Process and Dirichlet Process Mixture(PPT)

　• 理解共轭先验

　Google Three Papers Google 三大篇，HDFS，MapReduce，BigTable，奠定大数据基础架构的三篇文章，任何从事大数据行业的工程师都应该了解 • MapReduce Simplified Data Processing on Large Clusters

　• The Google File System

　• Bigtable A Distributed Storage System for Structured Data

　Factorization Machines FM 因子分解机模型的相关 paper，在计算广告领域非常实用的模型 • FM PPT by CMU

　• Factorization Machines Rendle2010

　• libfm-1.42.manual

　• Scaling Factorization Machines to Relational Data

　• fastFM- A Library for Factorization Machines

　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)

　Budget Control 广告系统中 Pacing，预算控制，以及怎么把预算控制与其他模块相结合的问题

　• Budget Pacing for Targeted Online Advertisements at LinkedIn

　• Predicting Traffic of Online Advertising in Real-time Bidding Systems from Perspective of Demand-Side Platforms

　• Real Time Bid Optimization with Smooth Budget Delivery in Online Advertising

　• PID 控制经典培训教程

　• PID 控制原理与控制算法

　• Smart Pacing for Effective Online Ad Campaign Optimization

　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

　Guaranteed Contracts Ads 事实上，现在很多大的媒体主仍是合约广告系统，合约广告系统的在线分配，Yield Optimization，以及定价问题都是非常重要且有挑战性的问题 • A Dynamic Pricing Model for Unifying Programmatic Guarantee and Real-Time Bidding in Display Advertising

　• Pricing Guaranteed Contracts in Online Display Advertising

　• Risk-Aware Dynamic Reserve Prices of Programmatic Guarantee in Display Advertising

　• Pricing Guidance in Ad Sale Negotiations The PrintAds Example

　• Risk-Aware Revenue Maximization in Display Advertising

　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)

　Bidding Strategy 计算广告中广告定价，RTB 过程中广告出价策略的相关问题 • Research Frontier of Real-Time Bidding based Display Advertising

　• Budget Constrained Bidding by Model-free Reinforcement Learning in Display Advertising

　• Real-Time Bidding with Multi-Agent Reinforcement Learning in Display Advertising

　• Real-Time Bidding by Reinforcement Learning in Display Advertising

　• Combining Powers of Two Predictors in Optimizing Real-Time Bidding Strategy under Constrained Budget

　• Bid-aware Gradient Descent for Unbiased Learning with Censored Data in Display Advertising

　• Optimized Cost per Click in Taobao Display Advertising

　• Real-Time Bidding Algorithms for Performance-Based Display Ad Allocation

　• Deep Reinforcement Learning for Sponsored Search Real-time Bidding

　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

　Machine Learning Tutorial 机器学习方面一些非常实用的学习资料 • 各种回归的概念学习

　• 机器学习总图

　• Efficient Estimation of Word Representations in Vector Space

　• Rules of Machine Learning- Best Practices for ML Engineering

　• An introduction to ROC analysis

　• Deep Learning Tutorial

　• 广义线性模型

　• 贝叶斯统计学(PPT)

　• 关联规则基本算法及其应用

　Transfer Learning 迁移学习相关文章，计算广告中经常遇到新广告冷启动的问题，利用迁移学习能较好解决该问题 • [Multi-Task]An Overview of Multi-Task Learning in Deep Neural Networks

　• Scalable Hands-Free Transfer Learning for Online Advertising

　• A Survey on Transfer Learning

　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)

　Exploration and Exploitation 探索和利用，计算广告中非常经典，也是容易被大家忽视的问题，其实所有的广告系统都面临如何解决新广告主冷启动，以及在效果不好的情况下如何探索新的优质流量的问题，希望该目录下的几篇文章能够帮助到你 • An Empirical Evaluation of Thompson Sampling

　• Dynamic Online Pricing with Incomplete Information Using Multi-Armed Bandit Experiments

　• Finite-time Analysis of the Multiarmed Bandit Problem

　• A Fast and Simple Algorithm for Contextual Bandits

　• Customer Acquisition via Display Advertising Using MultiArmed Bandit Experiments

　• Mastering the game of Go with deep neural networks and tree search

　• Exploring compact reinforcement-learning representations with linear regression

　• Incentivizting Exploration in Reinforcement Learning with Deep Predictive Models

　• Bandit Algorithms Continued- UCB1

　• A Contextual-Bandit Approach to Personalized News Article Recommendation(LinUCB)

　• Exploitation and Exploration in a Performance based Contextual Advertising System

　• Bandit based Monte-Carlo Planning

　• Random Forest for the Contextual Bandit Problem

　• Unifying Count-Based Exploration and Intrinsic Motivation

　• Analysis of Thompson Sampling for the Multi-armed Bandit Problem

　• Thompson Sampling PPT

　• Hierarchical Deep Reinforcement Learning- Integrating Temporal Abstraction and Intrinsic Motivation

　• Exploration and Exploitation Problem by Wang Zhe

　• Exploration exploitation in Go UCT for Monte-Carlo Go

　• 对抗搜索、多臂老虎机问题、UCB 算法

　• Using Confidence Bounds for Exploitation-Exploration Trade-offs

　Allocation 广告流量的分配问题 • An Efficient Algorithm for Allocation of Guaranteed Display Advertising

　• Ad Serving Using a Compact Allocation Plan