推荐系统论文、学习资料、业界分享

动态更新工作中实现或者阅读过的推荐系统相关论文、学习资料和业界分享，作为自己工作的总结，也希望能为推荐系统相关行业的同学带来便利。 所有资料均来自于互联网，如有侵权，请联系王喆。同时欢迎对推荐系统感兴趣的同学与我讨论相关问题，我的联系方式如下：

• Email: wzhe06@gmail.com
• LinkedIn: 王喆的LinkedIn
• 知乎私信: 王喆的知乎

其他相关资源

• 计算广告相关论文和资源列表
• 张伟楠的RTB Papers列表
• 基于Spark MLlib的CTR prediction模型(LR, Random forest, GBDT, NN, PNN)
• Honglei Zhang的推荐系统论文列表

目录

Deep Learning Recommender System

• [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)
• [DL Recsys Intro] Deep Learning based Recommender System- A Survey and New Perspectives (UNSW 2018)
• [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)
• [CDL] Collaborative Deep Learning for Recommender Systems (HKUST, 2015)
• [DSSM in Recsys] A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems (Microsoft 2015)
• [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)
• [Wide&Deep] Wide & Deep Learning for Recommender Systems (Google 2016)
• [DSSM] Learning Deep Structured Semantic Models for Web Search using Clickthrough Data (UIUC 2013)
• [NCF] Neural Collaborative Filtering (NUS 2017)
• [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)
• [Latent Cross] Latent Cross- Making Use of Context in Recurrent Recommender Systems (Google 2018)

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)
• [LSH] Locality-Sensitive Hashing for Finding Nearest Neighbors (IEEE 2008)
• [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)

Famous Machine Learning Papers

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

Classic Recommender System

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

Evaluation

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

Reinforcement Learning in Reco

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

Industry Recommender System

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

Exploration and Exploitation

• [EE in Ads] Customer Acquisition via Display Advertising Using MultiArmed Bandit Experiments (UMich 2015)
• [EE in Ads] Exploitation and Exploration in a Performance based Contextual Advertising System (Yahoo 2010)
• [EE in AlphaGo]Mastering the game of Go with deep neural networks and tree search (Deepmind 2016)
• [UCB1] Bandit Algorithms Continued - UCB1 (Noel Welsh 2010)
• [Spotify] Explore, Exploit, and Explain- Personalizing Explainable Recommendations with Bandits (Spotify 2018)
• [TS Intro] Thompson Sampling Slides (Berkeley 2010)
• [Thompson Sampling] An Empirical Evaluation of Thompson Sampling (Yahoo 2011)
• [UCT] Exploration exploitation in Go UCT for Monte-Carlo Go (UPSUD 2016)
• [LinUCB] A Contextual-Bandit Approach to Personalized News Article Recommendation (Yahoo 2010)
• [RF in MAB]Random Forest for the Contextual Bandit Problem (Orange 2016)
• [EE Intro] Exploration and Exploitation Problem Introduction by Wang Zhe (Hulu 2017)

Reco-papers

原文：https://www.cnblogs.com/cx2016/p/13086878.html