Awesome
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 <br />
- 张伟楠的RTB Papers列表<br />
- 基于Spark MLlib的CTR prediction模型(LR, Random forest, GBDT, NN, PNN) <br />
- 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) <br />
- [Deep Crossing] Deep Crossing - Web-Scale Modeling without Manually Crafted Combinatorial Features (Microsoft 2016) <br />
- [PNN] Product-based Neural Networks for User Response Prediction (SJTU 2016) <br />
- [DIN] Deep Interest Network for Click-Through Rate Prediction (Alibaba 2018) <br />
- [ESMM] Entire Space Multi-Task Model - An Effective Approach for Estimating Post-Click Conversion Rate (Alibaba 2018) <br />
- [DL Recsys Intro] Deep Learning based Recommender System- A Survey and New Perspectives (UNSW 2018) <br />
- [xDeepFM] xDeepFM - Combining Explicit and Implicit Feature Interactions for Recommender Systems (USTC 2018) <br />
- [Image CTR] Image Matters - Visually modeling user behaviors using Advanced Model Server (Alibaba 2018) <br />
- [CDL] Collaborative Deep Learning for Recommender Systems (HKUST, 2015) <br />
- [DSSM in Recsys] A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems (Microsoft 2015) <br />
- [AFM] Attentional Factorization Machines - Learning the Weight of Feature Interactions via Attention Networks (ZJU 2017) <br />
- [DIEN] Deep Interest Evolution Network for Click-Through Rate Prediction (Alibaba 2019) <br />
- [Wide&Deep] Wide & Deep Learning for Recommender Systems (Google 2016) <br />
- [DSSM] Learning Deep Structured Semantic Models for Web Search using Clickthrough Data (UIUC 2013) <br />
- [NCF] Neural Collaborative Filtering (NUS 2017) <br />
- [FNN] Deep Learning over Multi-field Categorical Data (UCL 2016) <br />
- [DeepFM] A Factorization-Machine based Neural Network for CTR Prediction (HIT-Huawei 2017) <br />
- [NFM] Neural Factorization Machines for Sparse Predictive Analytics (NUS 2017) <br />
- [Latent Cross] Latent Cross- Making Use of Context in Recurrent Recommender Systems (Google 2018) <br />
Embedding
- [Negative Sampling] Word2vec Explained Negative-Sampling Word-Embedding Method (2014) <br />
- [SDNE] Structural Deep Network Embedding (THU 2016) <br />
- [Item2Vec] Item2Vec-Neural Item Embedding for Collaborative Filtering (Microsoft 2016) <br />
- [Word2Vec] Distributed Representations of Words and Phrases and their Compositionality (Google 2013) <br />
- [LSH] Locality-Sensitive Hashing for Finding Nearest Neighbors (IEEE 2008) <br />
- [Word2Vec] Word2vec Parameter Learning Explained (UMich 2016) <br />
- [Node2vec] Node2vec - Scalable Feature Learning for Networks (Stanford 2016) <br />
- [Graph Embedding] DeepWalk- Online Learning of Social Representations (SBU 2014) <br />
- [Airbnb Embedding] Real-time Personalization using Embeddings for Search Ranking at Airbnb (Airbnb 2018) <br />
- [Alibaba Embedding] Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba (Alibaba 2018) <br />
- [Word2Vec] Efficient Estimation of Word Representations in Vector Space (Google 2013) <br />
- [LINE] LINE - Large-scale Information Network Embedding (MSRA 2015) <br />
Famous Machine Learning Papers
- [RNN] Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation (UofM 2014) <br />
- [CNN] ImageNet Classification with Deep Convolutional Neural Networks (UofT 2012) <br />
Classic Recommender System
- [MF] Matrix Factorization Techniques for Recommender Systems (Yahoo 2009) <br />
- [Earliest CF] Using Collaborative Filtering to Weave an Information Tapestry (PARC 1992) <br />
- [Recsys Intro] Recommender Systems Handbook (FRicci 2011) <br />
- [Recsys Intro slides] Recommender Systems An introduction (DJannach 2014) <br />
- [CF] Amazon Recommendations Item-to-Item Collaborative Filtering (Amazon 2003) <br />
- [ItemCF] Item-Based Collaborative Filtering Recommendation Algorithms (UMN 2001) <br />
- [Bilinear] Personalized Recommendation on Dynamic Content Using Predictive Bilinear Models (Yahoo 2009) <br />
Evaluation
- [EE Evaluation Intro] Offline Evaluation and Optimization for Interactive Systems (Microsoft 2015) <br />
- [Bootstrapped Replay] Improving offline evaluation of contextual bandit algorithms via bootstrapping techniques (Ulille 2014) <br />
- [InterLeaving] Large-Scale Validation and Analysis of Interleaved Search Evaluation (Yahoo 2012) <br />
- [Replay] Unbiased Offline Evaluation of Contextual-bandit-based News Article Recommendation Algorithms (Yahoo 2012) <br />
- [Classic Metrics] A Survey of Accuracy Evaluation Metrics of Recommendation Tasks (Microsoft 2009) <br />
Reinforcement Learning in Reco
- Active Learning in Collaborative Filtering Recommender Systems(UNIBZ 2014) <br />
- DRN- A Deep Reinforcement Learning Framework for News Recommendation (MSRA 2018) <br />
- Exploration in Interactive Personalized Music Recommendation- A Reinforcement Learning Approach (NUS 2013) <br />
- A survey of active learning in collaborative filtering recommender systems (POLIMI 2016) <br />
Industry Recommender System
- [Pinterest] Personalized content blending In the Pinterest home feed (Pinterest 2016) <br />
- [Pinterest] Graph Convolutional Neural Networks for Web-Scale Recommender Systems (Pinterest 2018) <br />
- [Airbnb] Search Ranking and Personalization at Airbnb Slides (Airbnb 2018) <br />
- [Baidu slides] DNN in Baidu Ads (Baidu 2017) <br />
- [Quora] Building a Machine Learning Platform at Quora (Quora 2016) <br />
- [Netflix] The Netflix Recommender System- Algorithms, Business Value, and Innovation (Netflix 2015) <br />
- [Youtube] Deep Neural Networks for YouTube Recommendations (Youtube 2016) <br />
- [Airbnb] Applying Deep Learning To Airbnb Search (Airbnb 2018) <br />
Exploration and Exploitation
- [EE in Ads] Customer Acquisition via Display Advertising Using MultiArmed Bandit Experiments (UMich 2015) <br />
- [EE in Ads] Exploitation and Exploration in a Performance based Contextual Advertising System (Yahoo 2010) <br />
- [EE in AlphaGo]Mastering the game of Go with deep neural networks and tree search (Deepmind 2016) <br />
- [UCB1] Bandit Algorithms Continued - UCB1 (Noel Welsh 2010) <br />
- [Spotify] Explore, Exploit, and Explain- Personalizing Explainable Recommendations with Bandits (Spotify 2018) <br />
- [TS Intro] Thompson Sampling Slides (Berkeley 2010) <br />
- [Thompson Sampling] An Empirical Evaluation of Thompson Sampling (Yahoo 2011) <br />
- [UCT] Exploration exploitation in Go UCT for Monte-Carlo Go (UPSUD 2016) <br />
- [LinUCB] A Contextual-Bandit Approach to Personalized News Article Recommendation (Yahoo 2010) <br />
- [RF in MAB]Random Forest for the Contextual Bandit Problem (Orange 2016) <br />
- [EE Intro] Exploration and Exploitation Problem Introduction by Wang Zhe (Hulu 2017) <br />