├── Classic Recommender System ├── [Bilinear] Personalized Recommendation on Dynamic Content Using Predictive Bilinear Models (Yahoo 2009).pdf ├── [CF] Amazon Recommendations Item-to-Item Collaborative Filtering (Amazon 2003).pdf ├── [Earliest CF] Using Collaborative Filtering to Weave an Information Tapestry (PARC 1992).pdf ├── [ItemCF] Item-Based Collaborative Filtering Recommendation Algorithms (UMN 2001).pdf ├── [MF] Matrix Factorization Techniques for Recommender Systems (Yahoo 2009).pdf ├── [Recsys Intro slides] Recommender Systems An introduction (DJannach 2014).pdf └── [Recsys Intro] Recommender Systems Handbook (FRicci 2011).pdf ├── Deep Learning Recommender System ├── [AFM] Attentional Factorization Machines - Learning the Weight of Feature Interactions via Attention Networks (ZJU 2017).pdf ├── [CDL] Collaborative Deep Learning for Recommender Systems (HKUST, 2015).pdf ├── [DCN] Deep & Cross Network for Ad Click Predictions (Stanford 2017).pdf ├── [DIEN] Deep Interest Evolution Network for Click-Through Rate Prediction (Alibaba 2019).pdf ├── [DIN] Deep Interest Network for Click-Through Rate Prediction (Alibaba 2018).pdf ├── [DL Recsys Intro] Deep Learning based Recommender System- A Survey and New Perspectives (UNSW 2018).pdf ├── [DSSM in Recsys] A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems (Microsoft 2015).pdf ├── [DSSM] Learning Deep Structured Semantic Models for Web Search using Clickthrough Data (UIUC 2013).pdf ├── [Deep Crossing] Deep Crossing - Web-Scale Modeling without Manually Crafted Combinatorial Features (Microsoft 2016).pdf ├── [DeepFM] A Factorization-Machine based Neural Network for CTR Prediction (HIT-Huawei 2017).pdf ├── [ESMM] Entire Space Multi-Task Model - An Effective Approach for Estimating Post-Click Conversion Rate (Alibaba 2018).pdf ├── [FNN] Deep Learning over Multi-field Categorical Data (UCL 2016).pdf ├── [Image CTR] Image Matters - Visually modeling user behaviors using Advanced Model Server (Alibaba 2018).pdf ├── [Latent Cross] Latent Cross- Making Use of Context in Recurrent Recommender Systems (Google 2018).pdf ├── [NCF] Neural Collaborative Filtering (NUS 2017).pdf ├── [NFM] Neural Factorization Machines for Sparse Predictive Analytics (NUS 2017).pdf ├── [PNN] Product-based Neural Networks for User Response Prediction (SJTU 2016).pdf ├── [Wide&Deep] Wide & Deep Learning for Recommender Systems (Google 2016).pdf └── [xDeepFM] xDeepFM - Combining Explicit and Implicit Feature Interactions for Recommender Systems (USTC 2018).pdf ├── Embedding ├── [Airbnb Embedding] Real-time Personalization using Embeddings for Search Ranking at Airbnb (Airbnb 2018).pdf ├── [Alibaba Embedding] Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba (Alibaba 2018).pdf ├── [Graph Embedding] DeepWalk- Online Learning of Social Representations (SBU 2014).pdf ├── [Item2Vec] Item2Vec-Neural Item Embedding for Collaborative Filtering (Microsoft 2016).pdf ├── [LINE] LINE - Large-scale Information Network Embedding (MSRA 2015).pdf ├── [LSH] Locality-Sensitive Hashing for Finding Nearest Neighbors (IEEE 2008).pdf ├── [Negative Sampling] Word2vec Explained Negative-Sampling Word-Embedding Method (2014).pdf ├── [Node2vec] Node2vec - Scalable Feature Learning for Networks (Stanford 2016).pdf ├── [SDNE] Structural Deep Network Embedding (THU 2016).pdf ├── [Word2Vec] Distributed Representations of Words and Phrases and their Compositionality (Google 2013).pdf ├── [Word2Vec] Efficient Estimation of Word Representations in Vector Space (Google 2013).pdf └── [Word2Vec] Word2vec Parameter Learning Explained (UMich 2016).pdf ├── Evaluation ├── [Bootstrapped Replay] Improving offline evaluation of contextual bandit algorithms via bootstrapping techniques (Ulille 2014).pdf ├── [Classic Metrics] A Survey of Accuracy Evaluation Metrics of Recommendation Tasks (Microsoft 2009).pdf ├── [EE Evaluation Intro] Offline Evaluation and Optimization for Interactive Systems (Microsoft 2015).pdf ├── [InterLeaving] Large-Scale Validation and Analysis of Interleaved Search Evaluation (Yahoo 2012).pdf └── [Replay] Unbiased Offline Evaluation of Contextual-bandit-based News Article Recommendation Algorithms (Yahoo 2012).pdf ├── Exploration and Exploitation ├── [EE Intro] Exploration and Exploitation Problem Introduction by Wang Zhe (Hulu 2017).pdf ├── [EE in Ads] Customer Acquisition via Display Advertising Using MultiArmed Bandit Experiments (UMich 2015).pdf ├── [EE in Ads] Exploitation and Exploration in a Performance based Contextual Advertising System (Yahoo 2010).pdf ├── [EE in AlphaGo]Mastering the game of Go with deep neural networks and tree search (Deepmind 2016).pdf ├── [LinUCB] A Contextual-Bandit Approach to Personalized News Article Recommendation (Yahoo 2010).pdf ├── [RF in MAB]Random Forest for the Contextual Bandit Problem (Orange 2016).pdf ├── [Spotify] Explore, Exploit, and Explain- Personalizing Explainable Recommendations with Bandits (Spotify 2018).pdf ├── [TS Intro] Thompson Sampling Slides (Berkeley 2010).pdf ├── [Thompson Sampling] An Empirical Evaluation of Thompson Sampling (Yahoo 2011).pdf ├── [UCB1] Bandit Algorithms Continued - UCB1 (Noel Welsh 2010).pdf └── [UCT] Exploration exploitation in Go UCT for Monte-Carlo Go (UPSUD 2016).pdf ├── Famous Machine Learning Papers ├── [CNN] ImageNet Classification with Deep Convolutional Neural Networks (UofT 2012).pdf └── [RNN] Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation (UofM 2014).pdf ├── Industry Recommender System ├── [Airbnb] Applying Deep Learning To Airbnb Search (Airbnb 2018).pdf ├── [Airbnb] Search Ranking and Personalization at Airbnb Slides (Airbnb 2018).pdf ├── [Baidu slides] DNN in Baidu Ads (Baidu 2017).pdf ├── [Netflix] The Netflix Recommender System- Algorithms, Business Value, and Innovation (Netflix 2015).pdf ├── [Pinterest] Graph Convolutional Neural Networks for Web-Scale Recommender Systems (Pinterest 2018).pdf ├── [Pinterest] Personalized content blending In the Pinterest home feed (Pinterest 2016).pdf ├── [Quora] Building a Machine Learning Platform at Quora (Quora 2016).pdf └── [Youtube] Deep Neural Networks for YouTube Recommendations (Youtube 2016).pdf ├── LICENSE ├── README.md ├── Reinforcement Learning in Reco ├── A survey of active learning in collaborative filtering recommender systems (POLIMI 2016).pdf ├── Active Learning in Collaborative Filtering Recommender Systems(UNIBZ 2014).pdf ├── DRN- A Deep Reinforcement Learning Framework for News Recommendation (MSRA 2018).pdf └── Exploration in Interactive Personalized Music Recommendation- A Reinforcement Learning Approach (NUS 2013).pdf ├── _config.yml └── generateReadme.py /Classic Recommender System/[Bilinear] Personalized Recommendation on Dynamic Content Using Predictive Bilinear Models (Yahoo 2009).pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wzhe06/Reco-papers/787969668c5f3efe1239dca1136e95da3cc1ed97/Classic Recommender System/[Bilinear] Personalized Recommendation on Dynamic Content Using Predictive Bilinear Models (Yahoo 2009).pdf -------------------------------------------------------------------------------- /Classic Recommender System/[CF] Amazon Recommendations Item-to-Item Collaborative Filtering (Amazon 2003).pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wzhe06/Reco-papers/787969668c5f3efe1239dca1136e95da3cc1ed97/Classic Recommender System/[CF] Amazon Recommendations Item-to-Item Collaborative Filtering (Amazon 2003).pdf -------------------------------------------------------------------------------- /Classic Recommender System/[Earliest CF] Using Collaborative Filtering to Weave an Information Tapestry (PARC 1992).pdf: -------------------------------------------------------------------------------- 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https://raw.githubusercontent.com/wzhe06/Reco-papers/787969668c5f3efe1239dca1136e95da3cc1ed97/Classic Recommender System/[MF] Matrix Factorization Techniques for Recommender Systems (Yahoo 2009).pdf -------------------------------------------------------------------------------- /Classic Recommender System/[Recsys Intro slides] Recommender Systems An introduction (DJannach 2014).pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wzhe06/Reco-papers/787969668c5f3efe1239dca1136e95da3cc1ed97/Classic Recommender System/[Recsys Intro slides] Recommender Systems An introduction (DJannach 2014).pdf -------------------------------------------------------------------------------- /Classic Recommender System/[Recsys Intro] Recommender Systems Handbook (FRicci 2011).pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wzhe06/Reco-papers/787969668c5f3efe1239dca1136e95da3cc1ed97/Classic Recommender System/[Recsys Intro] Recommender Systems Handbook (FRicci 2011).pdf -------------------------------------------------------------------------------- /Deep Learning Recommender System/[AFM] Attentional Factorization Machines - Learning the Weight of Feature Interactions via Attention Networks (ZJU 2017).pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wzhe06/Reco-papers/787969668c5f3efe1239dca1136e95da3cc1ed97/Deep Learning Recommender System/[AFM] Attentional Factorization Machines - Learning the Weight of Feature Interactions via Attention Networks (ZJU 2017).pdf -------------------------------------------------------------------------------- /Deep Learning Recommender System/[CDL] Collaborative Deep Learning for Recommender Systems (HKUST, 2015).pdf: 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2018).pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wzhe06/Reco-papers/787969668c5f3efe1239dca1136e95da3cc1ed97/Deep Learning Recommender System/[DL Recsys Intro] Deep Learning based Recommender System- A Survey and New Perspectives (UNSW 2018).pdf -------------------------------------------------------------------------------- /Deep Learning Recommender System/[DSSM in Recsys] A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems (Microsoft 2015).pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wzhe06/Reco-papers/787969668c5f3efe1239dca1136e95da3cc1ed97/Deep Learning Recommender System/[DSSM in Recsys] A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems (Microsoft 2015).pdf -------------------------------------------------------------------------------- /Deep Learning Recommender System/[DSSM] Learning Deep Structured Semantic Models for Web Search using Clickthrough Data (UIUC 2013).pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wzhe06/Reco-papers/787969668c5f3efe1239dca1136e95da3cc1ed97/Deep Learning Recommender System/[DSSM] Learning Deep Structured Semantic Models for Web Search using Clickthrough Data (UIUC 2013).pdf -------------------------------------------------------------------------------- /Deep Learning Recommender System/[Deep Crossing] Deep Crossing - Web-Scale Modeling without Manually Crafted Combinatorial Features (Microsoft 2016).pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wzhe06/Reco-papers/787969668c5f3efe1239dca1136e95da3cc1ed97/Deep Learning Recommender System/[Deep Crossing] Deep Crossing - Web-Scale Modeling without Manually Crafted Combinatorial Features (Microsoft 2016).pdf -------------------------------------------------------------------------------- /Deep Learning Recommender System/[DeepFM] A Factorization-Machine based Neural Network for CTR Prediction (HIT-Huawei 2017).pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wzhe06/Reco-papers/787969668c5f3efe1239dca1136e95da3cc1ed97/Deep Learning Recommender System/[DeepFM] A Factorization-Machine based Neural Network for CTR Prediction (HIT-Huawei 2017).pdf -------------------------------------------------------------------------------- /Deep Learning Recommender System/[ESMM] Entire Space Multi-Task Model - An Effective Approach for Estimating Post-Click Conversion Rate (Alibaba 2018).pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wzhe06/Reco-papers/787969668c5f3efe1239dca1136e95da3cc1ed97/Deep Learning Recommender System/[ESMM] Entire Space Multi-Task Model - An Effective Approach for Estimating Post-Click Conversion Rate (Alibaba 2018).pdf -------------------------------------------------------------------------------- /Deep Learning Recommender System/[FNN] Deep Learning over Multi-field Categorical Data (UCL 2016).pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wzhe06/Reco-papers/787969668c5f3efe1239dca1136e95da3cc1ed97/Deep Learning Recommender System/[FNN] Deep Learning over Multi-field Categorical Data (UCL 2016).pdf -------------------------------------------------------------------------------- /Deep Learning Recommender System/[Image CTR] Image Matters - Visually modeling user behaviors using Advanced Model Server (Alibaba 2018).pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wzhe06/Reco-papers/787969668c5f3efe1239dca1136e95da3cc1ed97/Deep Learning Recommender System/[Image CTR] Image Matters - Visually modeling user behaviors using Advanced Model Server (Alibaba 2018).pdf -------------------------------------------------------------------------------- /Deep Learning Recommender System/[Latent Cross] Latent Cross- Making Use of Context in Recurrent Recommender Systems (Google 2018).pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wzhe06/Reco-papers/787969668c5f3efe1239dca1136e95da3cc1ed97/Deep Learning Recommender System/[Latent Cross] Latent Cross- Making Use of Context in Recurrent Recommender Systems (Google 2018).pdf -------------------------------------------------------------------------------- /Deep Learning Recommender System/[NCF] Neural Collaborative Filtering (NUS 2017).pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wzhe06/Reco-papers/787969668c5f3efe1239dca1136e95da3cc1ed97/Deep Learning Recommender System/[NCF] Neural Collaborative Filtering (NUS 2017).pdf -------------------------------------------------------------------------------- /Deep Learning Recommender System/[NFM] Neural Factorization Machines for Sparse Predictive Analytics (NUS 2017).pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wzhe06/Reco-papers/787969668c5f3efe1239dca1136e95da3cc1ed97/Deep Learning Recommender System/[NFM] Neural Factorization Machines for Sparse Predictive Analytics (NUS 2017).pdf -------------------------------------------------------------------------------- /Deep Learning Recommender System/[PNN] Product-based Neural Networks for User Response Prediction (SJTU 2016).pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wzhe06/Reco-papers/787969668c5f3efe1239dca1136e95da3cc1ed97/Deep Learning Recommender System/[PNN] Product-based Neural Networks for User Response Prediction (SJTU 2016).pdf -------------------------------------------------------------------------------- /Deep Learning Recommender System/[Wide&Deep] Wide & Deep Learning for Recommender Systems (Google 2016).pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wzhe06/Reco-papers/787969668c5f3efe1239dca1136e95da3cc1ed97/Deep Learning Recommender System/[Wide&Deep] Wide & Deep Learning for Recommender Systems (Google 2016).pdf -------------------------------------------------------------------------------- /Deep Learning Recommender System/[xDeepFM] xDeepFM - Combining Explicit and Implicit Feature Interactions for Recommender Systems (USTC 2018).pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wzhe06/Reco-papers/787969668c5f3efe1239dca1136e95da3cc1ed97/Deep Learning Recommender System/[xDeepFM] xDeepFM - Combining Explicit and Implicit Feature Interactions for Recommender Systems (USTC 2018).pdf -------------------------------------------------------------------------------- /Embedding/[Airbnb Embedding] Real-time Personalization using Embeddings for Search Ranking at Airbnb (Airbnb 2018).pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wzhe06/Reco-papers/787969668c5f3efe1239dca1136e95da3cc1ed97/Embedding/[Airbnb Embedding] Real-time Personalization using Embeddings for Search Ranking at Airbnb (Airbnb 2018).pdf -------------------------------------------------------------------------------- /Embedding/[Alibaba Embedding] Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba (Alibaba 2018).pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wzhe06/Reco-papers/787969668c5f3efe1239dca1136e95da3cc1ed97/Embedding/[Alibaba Embedding] Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba (Alibaba 2018).pdf -------------------------------------------------------------------------------- /Embedding/[Graph Embedding] DeepWalk- Online Learning of Social Representations (SBU 2014).pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wzhe06/Reco-papers/787969668c5f3efe1239dca1136e95da3cc1ed97/Embedding/[Graph Embedding] DeepWalk- Online Learning of Social Representations (SBU 2014).pdf -------------------------------------------------------------------------------- /Embedding/[Item2Vec] Item2Vec-Neural Item Embedding for Collaborative Filtering (Microsoft 2016).pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wzhe06/Reco-papers/787969668c5f3efe1239dca1136e95da3cc1ed97/Embedding/[Item2Vec] Item2Vec-Neural Item Embedding for Collaborative Filtering (Microsoft 2016).pdf 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Negative-Sampling Word-Embedding Method (2014).pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wzhe06/Reco-papers/787969668c5f3efe1239dca1136e95da3cc1ed97/Embedding/[Negative Sampling] Word2vec Explained Negative-Sampling Word-Embedding Method (2014).pdf -------------------------------------------------------------------------------- /Embedding/[Node2vec] Node2vec - Scalable Feature Learning for Networks (Stanford 2016).pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wzhe06/Reco-papers/787969668c5f3efe1239dca1136e95da3cc1ed97/Embedding/[Node2vec] Node2vec - Scalable Feature Learning for Networks (Stanford 2016).pdf -------------------------------------------------------------------------------- /Embedding/[SDNE] Structural Deep Network Embedding (THU 2016).pdf: -------------------------------------------------------------------------------- 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https://raw.githubusercontent.com/wzhe06/Reco-papers/787969668c5f3efe1239dca1136e95da3cc1ed97/Embedding/[Word2Vec] Efficient Estimation of Word Representations in Vector Space (Google 2013).pdf -------------------------------------------------------------------------------- /Embedding/[Word2Vec] Word2vec Parameter Learning Explained (UMich 2016).pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wzhe06/Reco-papers/787969668c5f3efe1239dca1136e95da3cc1ed97/Embedding/[Word2Vec] Word2vec Parameter Learning Explained (UMich 2016).pdf -------------------------------------------------------------------------------- /Evaluation/[Bootstrapped Replay] Improving offline evaluation of contextual bandit algorithms via bootstrapping techniques (Ulille 2014).pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wzhe06/Reco-papers/787969668c5f3efe1239dca1136e95da3cc1ed97/Evaluation/[Bootstrapped Replay] Improving offline evaluation of contextual bandit algorithms via bootstrapping techniques (Ulille 2014).pdf -------------------------------------------------------------------------------- /Evaluation/[Classic Metrics] A Survey of Accuracy Evaluation Metrics of Recommendation Tasks (Microsoft 2009).pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wzhe06/Reco-papers/787969668c5f3efe1239dca1136e95da3cc1ed97/Evaluation/[Classic Metrics] A Survey of Accuracy Evaluation Metrics of Recommendation Tasks (Microsoft 2009).pdf -------------------------------------------------------------------------------- /Evaluation/[EE Evaluation Intro] Offline Evaluation and Optimization for Interactive Systems (Microsoft 2015).pdf: -------------------------------------------------------------------------------- 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Go with deep neural networks and tree search (Deepmind 2016).pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wzhe06/Reco-papers/787969668c5f3efe1239dca1136e95da3cc1ed97/Exploration and Exploitation/[EE in AlphaGo]Mastering the game of Go with deep neural networks and tree search (Deepmind 2016).pdf -------------------------------------------------------------------------------- /Exploration and Exploitation/[LinUCB] A Contextual-Bandit Approach to Personalized News Article Recommendation (Yahoo 2010).pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wzhe06/Reco-papers/787969668c5f3efe1239dca1136e95da3cc1ed97/Exploration and Exploitation/[LinUCB] A Contextual-Bandit Approach to Personalized News Article Recommendation (Yahoo 2010).pdf -------------------------------------------------------------------------------- /Exploration and Exploitation/[RF in MAB]Random Forest for the Contextual Bandit Problem (Orange 2016).pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wzhe06/Reco-papers/787969668c5f3efe1239dca1136e95da3cc1ed97/Exploration and Exploitation/[RF in MAB]Random Forest for the Contextual Bandit Problem (Orange 2016).pdf -------------------------------------------------------------------------------- /Exploration and Exploitation/[Spotify] Explore, Exploit, and Explain- Personalizing Explainable Recommendations with Bandits (Spotify 2018).pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wzhe06/Reco-papers/787969668c5f3efe1239dca1136e95da3cc1ed97/Exploration and Exploitation/[Spotify] Explore, Exploit, and Explain- Personalizing Explainable Recommendations with Bandits (Spotify 2018).pdf -------------------------------------------------------------------------------- /Exploration and Exploitation/[TS Intro] Thompson Sampling Slides (Berkeley 2010).pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wzhe06/Reco-papers/787969668c5f3efe1239dca1136e95da3cc1ed97/Exploration and Exploitation/[TS Intro] Thompson Sampling Slides (Berkeley 2010).pdf -------------------------------------------------------------------------------- /Exploration and Exploitation/[Thompson Sampling] An Empirical Evaluation of Thompson Sampling (Yahoo 2011).pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wzhe06/Reco-papers/787969668c5f3efe1239dca1136e95da3cc1ed97/Exploration and Exploitation/[Thompson Sampling] An Empirical Evaluation of Thompson Sampling (Yahoo 2011).pdf -------------------------------------------------------------------------------- /Exploration and Exploitation/[UCB1] Bandit Algorithms Continued - UCB1 (Noel Welsh 2010).pdf: 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Search (Airbnb 2018).pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wzhe06/Reco-papers/787969668c5f3efe1239dca1136e95da3cc1ed97/Industry Recommender System/[Airbnb] Applying Deep Learning To Airbnb Search (Airbnb 2018).pdf -------------------------------------------------------------------------------- /Industry Recommender System/[Airbnb] Search Ranking and Personalization at Airbnb Slides (Airbnb 2018).pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wzhe06/Reco-papers/787969668c5f3efe1239dca1136e95da3cc1ed97/Industry Recommender System/[Airbnb] Search Ranking and Personalization at Airbnb Slides (Airbnb 2018).pdf -------------------------------------------------------------------------------- /Industry Recommender System/[Baidu slides] DNN in Baidu Ads (Baidu 2017).pdf: 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granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # 推荐系统论文、学习资料、业界分享 2 | 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。 3 | 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： 4 | * Email: wzhe06@gmail.com 5 | * LinkedIn: [王喆的LinkedIn](https://www.linkedin.com/in/zhe-wang-profile/) 6 | * 知乎私信: [王喆的知乎](https://www.zhihu.com/people/wang-zhe-58) 7 | 8 | **其他相关资源** 9 | * [计算广告相关论文和资源列表](https://github.com/wzhe06/Ad-papers)
10 | * [张伟楠的RTB Papers列表](https://github.com/wnzhang/rtb-papers)
11 | * [基于Spark MLlib的CTR prediction模型(LR, Random forest, GBDT, NN, PNN)](https://github.com/wzhe06/CTRmodel)
12 | * [Honglei Zhang的推荐系统论文列表](https://github.com/hongleizhang/RSPapers) 13 | 14 | 15 | ## 目录 16 | 17 | ### Deep Learning Recommender System 18 | * [[DCN] Deep & Cross Network for Ad Click Predictions (Stanford 2017)](https://github.com/wzhe06/Reco-papers/blob/master/Deep%20Learning%20Recommender%20System/%5BDCN%5D%20Deep%20%26%20Cross%20Network%20for%20Ad%20Click%20Predictions%20%28Stanford%202017%29.pdf)
19 | * [[Deep Crossing] Deep Crossing - Web-Scale Modeling without Manually Crafted Combinatorial Features (Microsoft 2016)](https://github.com/wzhe06/Reco-papers/blob/master/Deep%20Learning%20Recommender%20System/%5BDeep%20Crossing%5D%20Deep%20Crossing%20-%20Web-Scale%20Modeling%20without%20Manually%20Crafted%20Combinatorial%20Features%20%28Microsoft%202016%29.pdf)
20 | * [[PNN] Product-based Neural Networks for User Response Prediction (SJTU 2016)](https://github.com/wzhe06/Reco-papers/blob/master/Deep%20Learning%20Recommender%20System/%5BPNN%5D%20Product-based%20Neural%20Networks%20for%20User%20Response%20Prediction%20%28SJTU%202016%29.pdf)
21 | * [[DIN] Deep Interest Network for Click-Through Rate Prediction (Alibaba 2018)](https://github.com/wzhe06/Reco-papers/blob/master/Deep%20Learning%20Recommender%20System/%5BDIN%5D%20Deep%20Interest%20Network%20for%20Click-Through%20Rate%20Prediction%20%28Alibaba%202018%29.pdf)
22 | * [[ESMM] Entire Space Multi-Task Model - An Effective Approach for Estimating Post-Click Conversion Rate (Alibaba 2018)](https://github.com/wzhe06/Reco-papers/blob/master/Deep%20Learning%20Recommender%20System/%5BESMM%5D%20Entire%20Space%20Multi-Task%20Model%20-%20An%20Effective%20Approach%20for%20Estimating%20Post-Click%20Conversion%20Rate%20%28Alibaba%202018%29.pdf)
23 | * [[DL Recsys Intro] Deep Learning based Recommender System- A Survey and New Perspectives (UNSW 2018)](https://github.com/wzhe06/Reco-papers/blob/master/Deep%20Learning%20Recommender%20System/%5BDL%20Recsys%20Intro%5D%20Deep%20Learning%20based%20Recommender%20System-%20A%20Survey%20and%20New%20Perspectives%20%28UNSW%202018%29.pdf)
24 | * [[xDeepFM] xDeepFM - Combining Explicit and Implicit Feature Interactions for Recommender Systems (USTC 2018)](https://github.com/wzhe06/Reco-papers/blob/master/Deep%20Learning%20Recommender%20System/%5BxDeepFM%5D%20xDeepFM%20-%20Combining%20Explicit%20and%20Implicit%20Feature%20Interactions%20for%20Recommender%20Systems%20%28USTC%202018%29.pdf)
25 | * [[Image CTR] Image Matters - Visually modeling user behaviors using Advanced Model Server (Alibaba 2018)](https://github.com/wzhe06/Reco-papers/blob/master/Deep%20Learning%20Recommender%20System/%5BImage%20CTR%5D%20Image%20Matters%20-%20Visually%20modeling%20user%20behaviors%20using%20Advanced%20Model%20Server%20%28Alibaba%202018%29.pdf)
26 | * [[CDL] Collaborative Deep Learning for Recommender Systems (HKUST, 2015)](https://github.com/wzhe06/Reco-papers/blob/master/Deep%20Learning%20Recommender%20System/%5BCDL%5D%20Collaborative%20Deep%20Learning%20for%20Recommender%20Systems%20%28HKUST%2C%202015%29.pdf)
27 | * [[DSSM in Recsys] A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems (Microsoft 2015)](https://github.com/wzhe06/Reco-papers/blob/master/Deep%20Learning%20Recommender%20System/%5BDSSM%20in%20Recsys%5D%20A%20Multi-View%20Deep%20Learning%20Approach%20for%20Cross%20Domain%20User%20Modeling%20in%20Recommendation%20Systems%20%28Microsoft%202015%29.pdf)
28 | * [[AFM] Attentional Factorization Machines - Learning the Weight of Feature Interactions via Attention Networks (ZJU 2017)](https://github.com/wzhe06/Reco-papers/blob/master/Deep%20Learning%20Recommender%20System/%5BAFM%5D%20Attentional%20Factorization%20Machines%20-%20Learning%20the%20Weight%20of%20Feature%20Interactions%20via%20Attention%20Networks%20%28ZJU%202017%29.pdf)
29 | * [[DIEN] Deep Interest Evolution Network for Click-Through Rate Prediction (Alibaba 2019)](https://github.com/wzhe06/Reco-papers/blob/master/Deep%20Learning%20Recommender%20System/%5BDIEN%5D%20Deep%20Interest%20Evolution%20Network%20for%20Click-Through%20Rate%20Prediction%20%28Alibaba%202019%29.pdf)
30 | * [[Wide&Deep] Wide & Deep Learning for Recommender Systems (Google 2016)](https://github.com/wzhe06/Reco-papers/blob/master/Deep%20Learning%20Recommender%20System/%5BWide%26Deep%5D%20Wide%20%26%20Deep%20Learning%20for%20Recommender%20Systems%20%28Google%202016%29.pdf)
31 | * [[DSSM] Learning Deep Structured Semantic Models for Web Search using Clickthrough Data (UIUC 2013)](https://github.com/wzhe06/Reco-papers/blob/master/Deep%20Learning%20Recommender%20System/%5BDSSM%5D%20Learning%20Deep%20Structured%20Semantic%20Models%20for%20Web%20Search%20using%20Clickthrough%20Data%20%28UIUC%202013%29.pdf)
32 | * [[NCF] Neural Collaborative Filtering (NUS 2017)](https://github.com/wzhe06/Reco-papers/blob/master/Deep%20Learning%20Recommender%20System/%5BNCF%5D%20Neural%20Collaborative%20Filtering%20%28NUS%202017%29.pdf)
33 | * [[FNN] Deep Learning over Multi-field Categorical Data (UCL 2016)](https://github.com/wzhe06/Reco-papers/blob/master/Deep%20Learning%20Recommender%20System/%5BFNN%5D%20Deep%20Learning%20over%20Multi-field%20Categorical%20Data%20%28UCL%202016%29.pdf)
34 | * [[DeepFM] A Factorization-Machine based Neural Network for CTR Prediction (HIT-Huawei 2017)](https://github.com/wzhe06/Reco-papers/blob/master/Deep%20Learning%20Recommender%20System/%5BDeepFM%5D%20A%20Factorization-Machine%20based%20Neural%20Network%20for%20CTR%20Prediction%20%28HIT-Huawei%202017%29.pdf)
35 | * [[NFM] Neural Factorization Machines for Sparse Predictive Analytics (NUS 2017)](https://github.com/wzhe06/Reco-papers/blob/master/Deep%20Learning%20Recommender%20System/%5BNFM%5D%20Neural%20Factorization%20Machines%20for%20Sparse%20Predictive%20Analytics%20%28NUS%202017%29.pdf)
36 | * [[Latent Cross] Latent Cross- Making Use of Context in Recurrent Recommender Systems (Google 2018)](https://github.com/wzhe06/Reco-papers/blob/master/Deep%20Learning%20Recommender%20System/%5BLatent%20Cross%5D%20Latent%20Cross-%20Making%20Use%20of%20Context%20in%20Recurrent%20Recommender%20Systems%20%28Google%202018%29.pdf)
37 | 38 | ### Embedding 39 | * [[Negative Sampling] Word2vec Explained Negative-Sampling Word-Embedding Method (2014)](https://github.com/wzhe06/Reco-papers/blob/master/Embedding/%5BNegative%20Sampling%5D%20Word2vec%20Explained%20Negative-Sampling%20Word-Embedding%20Method%20%282014%29.pdf)
40 | * [[SDNE] Structural Deep Network Embedding (THU 2016)](https://github.com/wzhe06/Reco-papers/blob/master/Embedding/%5BSDNE%5D%20Structural%20Deep%20Network%20Embedding%20%28THU%202016%29.pdf)
41 | * [[Item2Vec] Item2Vec-Neural Item Embedding for Collaborative Filtering (Microsoft 2016)](https://github.com/wzhe06/Reco-papers/blob/master/Embedding/%5BItem2Vec%5D%20Item2Vec-Neural%20Item%20Embedding%20for%20Collaborative%20Filtering%20%28Microsoft%202016%29.pdf)
42 | * [[Word2Vec] Distributed Representations of Words and Phrases and their Compositionality (Google 2013)](https://github.com/wzhe06/Reco-papers/blob/master/Embedding/%5BWord2Vec%5D%20Distributed%20Representations%20of%20Words%20and%20Phrases%20and%20their%20Compositionality%20%28Google%202013%29.pdf)
43 | * [[LSH] Locality-Sensitive Hashing for Finding Nearest Neighbors (IEEE 2008)](https://github.com/wzhe06/Reco-papers/blob/master/Embedding/%5BLSH%5D%20Locality-Sensitive%20Hashing%20for%20Finding%20Nearest%20Neighbors%20%28IEEE%202008%29.pdf)
44 | * [[Word2Vec] Word2vec Parameter Learning Explained (UMich 2016)](https://github.com/wzhe06/Reco-papers/blob/master/Embedding/%5BWord2Vec%5D%20Word2vec%20Parameter%20Learning%20Explained%20%28UMich%202016%29.pdf)
45 | * [[Node2vec] Node2vec - Scalable Feature Learning for Networks (Stanford 2016)](https://github.com/wzhe06/Reco-papers/blob/master/Embedding/%5BNode2vec%5D%20Node2vec%20-%20Scalable%20Feature%20Learning%20for%20Networks%20%28Stanford%202016%29.pdf)
46 | * [[Graph Embedding] DeepWalk- Online Learning of Social Representations (SBU 2014)](https://github.com/wzhe06/Reco-papers/blob/master/Embedding/%5BGraph%20Embedding%5D%20DeepWalk-%20Online%20Learning%20of%20Social%20Representations%20%28SBU%202014%29.pdf)
47 | * [[Airbnb Embedding] Real-time Personalization using Embeddings for Search Ranking at Airbnb (Airbnb 2018)](https://github.com/wzhe06/Reco-papers/blob/master/Embedding/%5BAirbnb%20Embedding%5D%20Real-time%20Personalization%20using%20Embeddings%20for%20Search%20Ranking%20at%20Airbnb%20%28Airbnb%202018%29.pdf)
48 | * [[Alibaba Embedding] Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba (Alibaba 2018)](https://github.com/wzhe06/Reco-papers/blob/master/Embedding/%5BAlibaba%20Embedding%5D%20Billion-scale%20Commodity%20Embedding%20for%20E-commerce%20Recommendation%20in%20Alibaba%20%28Alibaba%202018%29.pdf)
49 | * [[Word2Vec] Efficient Estimation of Word Representations in Vector Space (Google 2013)](https://github.com/wzhe06/Reco-papers/blob/master/Embedding/%5BWord2Vec%5D%20Efficient%20Estimation%20of%20Word%20Representations%20in%20Vector%20Space%20%28Google%202013%29.pdf)
50 | * [[LINE] LINE - Large-scale Information Network Embedding (MSRA 2015)](https://github.com/wzhe06/Reco-papers/blob/master/Embedding/%5BLINE%5D%20LINE%20-%20Large-scale%20Information%20Network%20Embedding%20%28MSRA%202015%29.pdf)
51 | 52 | ### Famous Machine Learning Papers 53 | * [[RNN] Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation (UofM 2014)](https://github.com/wzhe06/Reco-papers/blob/master/Famous%20Machine%20Learning%20Papers/%5BRNN%5D%20Learning%20Phrase%20Representations%20using%20RNN%20Encoder%E2%80%93Decoder%20for%20Statistical%20Machine%20Translation%20%28UofM%202014%29.pdf)
54 | * [[CNN] ImageNet Classification with Deep Convolutional Neural Networks (UofT 2012)](https://github.com/wzhe06/Reco-papers/blob/master/Famous%20Machine%20Learning%20Papers/%5BCNN%5D%20ImageNet%20Classification%20with%20Deep%20Convolutional%20Neural%20Networks%20%28UofT%202012%29.pdf)
55 | 56 | ### Classic Recommender System 57 | * [[MF] Matrix Factorization Techniques for Recommender Systems (Yahoo 2009)](https://github.com/wzhe06/Reco-papers/blob/master/Classic%20Recommender%20System/%5BMF%5D%20Matrix%20Factorization%20Techniques%20for%20Recommender%20Systems%20%28Yahoo%202009%29.pdf)
58 | * [[Earliest CF] Using Collaborative Filtering to Weave an Information Tapestry (PARC 1992)](https://github.com/wzhe06/Reco-papers/blob/master/Classic%20Recommender%20System/%5BEarliest%20CF%5D%20Using%20Collaborative%20Filtering%20to%20Weave%20an%20Information%20Tapestry%20%28PARC%201992%29.pdf)
59 | * [[Recsys Intro] Recommender Systems Handbook (FRicci 2011)](https://github.com/wzhe06/Reco-papers/blob/master/Classic%20Recommender%20System/%5BRecsys%20Intro%5D%20Recommender%20Systems%20Handbook%20%28FRicci%202011%29.pdf)
60 | * [[Recsys Intro slides] Recommender Systems An introduction (DJannach 2014)](https://github.com/wzhe06/Reco-papers/blob/master/Classic%20Recommender%20System/%5BRecsys%20Intro%20slides%5D%20Recommender%20Systems%20An%20introduction%20%28DJannach%202014%29.pdf)
61 | * [[CF] Amazon Recommendations Item-to-Item Collaborative Filtering (Amazon 2003)](https://github.com/wzhe06/Reco-papers/blob/master/Classic%20Recommender%20System/%5BCF%5D%20Amazon%20Recommendations%20Item-to-Item%20Collaborative%20Filtering%20%28Amazon%202003%29.pdf)
62 | * [[ItemCF] Item-Based Collaborative Filtering Recommendation Algorithms (UMN 2001)](https://github.com/wzhe06/Reco-papers/blob/master/Classic%20Recommender%20System/%5BItemCF%5D%20Item-Based%20Collaborative%20Filtering%20Recommendation%20Algorithms%20%28UMN%202001%29.pdf)
63 | * [[Bilinear] Personalized Recommendation on Dynamic Content Using Predictive Bilinear Models (Yahoo 2009)](https://github.com/wzhe06/Reco-papers/blob/master/Classic%20Recommender%20System/%5BBilinear%5D%20Personalized%20Recommendation%20on%20Dynamic%20Content%20Using%20Predictive%20Bilinear%20Models%20%28Yahoo%202009%29.pdf)
64 | 65 | ### Evaluation 66 | * [[EE Evaluation Intro] Offline Evaluation and Optimization for Interactive Systems (Microsoft 2015)](https://github.com/wzhe06/Reco-papers/blob/master/Evaluation/%5BEE%20Evaluation%20Intro%5D%20Offline%C2%A0Evaluation%C2%A0and%C2%A0Optimization%20for%C2%A0Interactive%C2%A0Systems%20%28Microsoft%202015%29.pdf)
67 | * [[Bootstrapped Replay] Improving offline evaluation of contextual bandit algorithms via bootstrapping techniques (Ulille 2014)](https://github.com/wzhe06/Reco-papers/blob/master/Evaluation/%5BBootstrapped%20Replay%5D%20Improving%20offline%20evaluation%20of%20contextual%20bandit%20algorithms%20via%20bootstrapping%20techniques%20%28Ulille%202014%29.pdf)
68 | * [[InterLeaving] Large-Scale Validation and Analysis of Interleaved Search Evaluation (Yahoo 2012)](https://github.com/wzhe06/Reco-papers/blob/master/Evaluation/%5BInterLeaving%5D%20Large-Scale%20Validation%20and%20Analysis%20of%20Interleaved%20Search%20Evaluation%20%28Yahoo%202012%29.pdf)
69 | * [[Replay] Unbiased Offline Evaluation of Contextual-bandit-based News Article Recommendation Algorithms (Yahoo 2012)](https://github.com/wzhe06/Reco-papers/blob/master/Evaluation/%5BReplay%5D%20Unbiased%20Offline%20Evaluation%20of%20Contextual-bandit-based%20News%20Article%20Recommendation%20Algorithms%20%28Yahoo%202012%29.pdf)
70 | * [[Classic Metrics] A Survey of Accuracy Evaluation Metrics of Recommendation Tasks (Microsoft 2009)](https://github.com/wzhe06/Reco-papers/blob/master/Evaluation/%5BClassic%20Metrics%5D%20A%20Survey%20of%20Accuracy%20Evaluation%20Metrics%20of%20Recommendation%20Tasks%20%28Microsoft%202009%29.pdf)
71 | 72 | ### Reinforcement Learning in Reco 73 | * [Active Learning in Collaborative Filtering Recommender Systems(UNIBZ 2014)](https://github.com/wzhe06/Reco-papers/blob/master/Reinforcement%20Learning%20in%20Reco/Active%20Learning%20in%20Collaborative%20Filtering%20Recommender%20Systems%28UNIBZ%202014%29.pdf)
74 | * [DRN- A Deep Reinforcement Learning Framework for News Recommendation (MSRA 2018)](https://github.com/wzhe06/Reco-papers/blob/master/Reinforcement%20Learning%20in%20Reco/DRN-%20A%20Deep%20Reinforcement%20Learning%20Framework%20for%20News%20Recommendation%20%28MSRA%202018%29.pdf)
75 | * [Exploration in Interactive Personalized Music Recommendation- A Reinforcement Learning Approach (NUS 2013)](https://github.com/wzhe06/Reco-papers/blob/master/Reinforcement%20Learning%20in%20Reco/Exploration%20in%20Interactive%20Personalized%20Music%20Recommendation-%20A%20Reinforcement%20Learning%20Approach%20%28NUS%202013%29.pdf)
76 | * [A survey of active learning in collaborative filtering recommender systems (POLIMI 2016)](https://github.com/wzhe06/Reco-papers/blob/master/Reinforcement%20Learning%20in%20Reco/A%20survey%20of%20active%20learning%20in%20collaborative%20filtering%20recommender%20systems%20%28POLIMI%202016%29.pdf)
77 | 78 | ### Industry Recommender System 79 | * [[Pinterest] Personalized content blending In the Pinterest home feed (Pinterest 2016)](https://github.com/wzhe06/Reco-papers/blob/master/Industry%20Recommender%20System/%5BPinterest%5D%20Personalized%20content%20blending%20In%20the%20Pinterest%20home%20feed%20%28Pinterest%202016%29.pdf)
80 | * [[Pinterest] Graph Convolutional Neural Networks for Web-Scale Recommender Systems (Pinterest 2018)](https://github.com/wzhe06/Reco-papers/blob/master/Industry%20Recommender%20System/%5BPinterest%5D%20Graph%20Convolutional%20Neural%20Networks%20for%20Web-Scale%20Recommender%20Systems%20%28Pinterest%202018%29.pdf)
81 | * [[Airbnb] Search Ranking and Personalization at Airbnb Slides (Airbnb 2018)](https://github.com/wzhe06/Reco-papers/blob/master/Industry%20Recommender%20System/%5BAirbnb%5D%20Search%20Ranking%20and%20Personalization%20at%20Airbnb%20Slides%20%28Airbnb%202018%29.pdf)
82 | * [[Baidu slides] DNN in Baidu Ads (Baidu 2017)](https://github.com/wzhe06/Reco-papers/blob/master/Industry%20Recommender%20System/%5BBaidu%20slides%5D%20DNN%20in%20Baidu%20Ads%20%28Baidu%202017%29.pdf)
83 | * [[Quora] Building a Machine Learning Platform at Quora (Quora 2016)](https://github.com/wzhe06/Reco-papers/blob/master/Industry%20Recommender%20System/%5BQuora%5D%20Building%20a%20Machine%20Learning%20Platform%20at%20Quora%20%28Quora%202016%29.pdf)
84 | * [[Netflix] The Netflix Recommender System- Algorithms, Business Value, and Innovation (Netflix 2015)](https://github.com/wzhe06/Reco-papers/blob/master/Industry%20Recommender%20System/%5BNetflix%5D%20The%20Netflix%20Recommender%20System-%20Algorithms%2C%20Business%20Value%2C%20and%20Innovation%20%28Netflix%202015%29.pdf)
85 | * [[Youtube] Deep Neural Networks for YouTube Recommendations (Youtube 2016)](https://github.com/wzhe06/Reco-papers/blob/master/Industry%20Recommender%20System/%5BYoutube%5D%20Deep%20Neural%20Networks%20for%20YouTube%20Recommendations%20%28Youtube%202016%29.pdf)
86 | * [[Airbnb] Applying Deep Learning To Airbnb Search (Airbnb 2018)](https://github.com/wzhe06/Reco-papers/blob/master/Industry%20Recommender%20System/%5BAirbnb%5D%20Applying%20Deep%20Learning%20To%20Airbnb%20Search%20%28Airbnb%202018%29.pdf)
87 | 88 | ### Exploration and Exploitation 89 | * [[EE in Ads] Customer Acquisition via Display Advertising Using MultiArmed Bandit Experiments (UMich 2015)](https://github.com/wzhe06/Reco-papers/blob/master/Exploration%20and%20Exploitation/%5BEE%20in%20Ads%5D%20Customer%20Acquisition%20via%20Display%20Advertising%20Using%20MultiArmed%20Bandit%20Experiments%20%28UMich%202015%29.pdf)
90 | * [[EE in Ads] Exploitation and Exploration in a Performance based Contextual Advertising System (Yahoo 2010)](https://github.com/wzhe06/Reco-papers/blob/master/Exploration%20and%20Exploitation/%5BEE%20in%20Ads%5D%20Exploitation%20and%20Exploration%20in%20a%20Performance%20based%20Contextual%20Advertising%20System%20%28Yahoo%202010%29.pdf)
91 | * [[EE in AlphaGo]Mastering the game of Go with deep neural networks and tree search (Deepmind 2016)](https://github.com/wzhe06/Reco-papers/blob/master/Exploration%20and%20Exploitation/%5BEE%20in%20AlphaGo%5DMastering%20the%20game%20of%20Go%20with%20deep%20neural%20networks%20and%20tree%20search%20%28Deepmind%202016%29.pdf)
92 | * [[UCB1] Bandit Algorithms Continued - UCB1 (Noel Welsh 2010)](https://github.com/wzhe06/Reco-papers/blob/master/Exploration%20and%20Exploitation/%5BUCB1%5D%20Bandit%20Algorithms%20Continued%20-%20UCB1%20%28Noel%20Welsh%202010%29.pdf)
93 | * [[Spotify] Explore, Exploit, and Explain- Personalizing Explainable Recommendations with Bandits (Spotify 2018)](https://github.com/wzhe06/Reco-papers/blob/master/Exploration%20and%20Exploitation/%5BSpotify%5D%20Explore%2C%20Exploit%2C%20and%20Explain-%20Personalizing%20Explainable%20Recommendations%20with%20Bandits%20%28Spotify%202018%29.pdf)
94 | * [[TS Intro] Thompson Sampling Slides (Berkeley 2010)](https://github.com/wzhe06/Reco-papers/blob/master/Exploration%20and%20Exploitation/%5BTS%20Intro%5D%20Thompson%20Sampling%20Slides%20%28Berkeley%202010%29.pdf)
95 | * [[Thompson Sampling] An Empirical Evaluation of Thompson Sampling (Yahoo 2011)](https://github.com/wzhe06/Reco-papers/blob/master/Exploration%20and%20Exploitation/%5BThompson%20Sampling%5D%20An%20Empirical%20Evaluation%20of%20Thompson%20Sampling%20%28Yahoo%202011%29.pdf)
96 | * [[UCT] Exploration exploitation in Go UCT for Monte-Carlo Go (UPSUD 2016)](https://github.com/wzhe06/Reco-papers/blob/master/Exploration%20and%20Exploitation/%5BUCT%5D%20Exploration%20exploitation%20in%20Go%20UCT%20for%20Monte-Carlo%20Go%20%28UPSUD%202016%29.pdf)
97 | * [[LinUCB] A Contextual-Bandit Approach to Personalized News Article Recommendation (Yahoo 2010)](https://github.com/wzhe06/Reco-papers/blob/master/Exploration%20and%20Exploitation/%5BLinUCB%5D%20A%20Contextual-Bandit%20Approach%20to%20Personalized%20News%20Article%20Recommendation%20%28Yahoo%202010%29.pdf)
98 | * [[RF in MAB]Random Forest for the Contextual Bandit Problem (Orange 2016)](https://github.com/wzhe06/Reco-papers/blob/master/Exploration%20and%20Exploitation/%5BRF%20in%20MAB%5DRandom%20Forest%20for%20the%20Contextual%20Bandit%20Problem%20%28Orange%202016%29.pdf)
99 | * [[EE Intro] Exploration and Exploitation Problem Introduction by Wang Zhe (Hulu 2017)](https://github.com/wzhe06/Reco-papers/blob/master/Exploration%20and%20Exploitation/%5BEE%20Intro%5D%20Exploration%20and%20Exploitation%20Problem%20Introduction%20by%20Wang%20Zhe%20%28Hulu%202017%29.pdf)
100 | -------------------------------------------------------------------------------- /Reinforcement Learning in Reco/A survey of active learning in collaborative filtering recommender systems (POLIMI 2016).pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wzhe06/Reco-papers/787969668c5f3efe1239dca1136e95da3cc1ed97/Reinforcement Learning in Reco/A survey of active learning in collaborative filtering recommender systems (POLIMI 2016).pdf -------------------------------------------------------------------------------- /Reinforcement Learning in Reco/Active Learning in Collaborative Filtering Recommender Systems(UNIBZ 2014).pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wzhe06/Reco-papers/787969668c5f3efe1239dca1136e95da3cc1ed97/Reinforcement Learning in Reco/Active Learning in Collaborative Filtering Recommender Systems(UNIBZ 2014).pdf -------------------------------------------------------------------------------- /Reinforcement Learning in Reco/DRN- A Deep Reinforcement Learning Framework for News Recommendation (MSRA 2018).pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wzhe06/Reco-papers/787969668c5f3efe1239dca1136e95da3cc1ed97/Reinforcement Learning in Reco/DRN- A Deep Reinforcement Learning Framework for News Recommendation (MSRA 2018).pdf -------------------------------------------------------------------------------- /Reinforcement Learning in Reco/Exploration in Interactive Personalized Music Recommendation- A Reinforcement Learning Approach (NUS 2013).pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wzhe06/Reco-papers/787969668c5f3efe1239dca1136e95da3cc1ed97/Reinforcement Learning in Reco/Exploration in Interactive Personalized Music Recommendation- A Reinforcement Learning Approach (NUS 2013).pdf -------------------------------------------------------------------------------- /_config.yml: -------------------------------------------------------------------------------- 1 | theme: jekyll-theme-cayman -------------------------------------------------------------------------------- /generateReadme.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | import os 4 | import urllib 5 | 6 | """ generate readme.md """ 7 | __author__ = 'Wang Zhe' 8 | 9 | paper_class_map = {} 10 | paper_map = {} 11 | 12 | file_object = open('./README.md') 13 | all_lines = file_object.readlines() 14 | file_object.close() 15 | 16 | out_file = open('./README.md', 'w') 17 | 18 | paper_class_flag = 0 19 | paper_class_name = "" 20 | paper_flag = 0 21 | paper_name = "" 22 | catalog_flag = 0 23 | 24 | for line in all_lines: 25 | if catalog_flag != 1: 26 | out_file.write(line) 27 | if line.startswith("##"): 28 | catalog_flag = 1 29 | 30 | if paper_class_flag == 1 and not line.startswith("*") and not line.startswith("#"): 31 | paper_class_map[paper_class_name] = line.strip() 32 | print paper_class_name, line.strip() 33 | paper_class_flag = 0 34 | 35 | if paper_flag == 1 and not line.startswith("*") and not line.startswith("#"): 36 | paper_map[paper_name] = line.strip() 37 | print "\t", paper_name, line.strip() 38 | 39 | paper_flag = 0 40 | 41 | if catalog_flag == 1: 42 | if line.startswith("*"): 43 | paper_flag = 1 44 | paper_name = line[line.find("[")+1:line.find("]")].strip() 45 | 46 | if line.startswith("###"): 47 | paper_class_flag = 1 48 | paper_class_name = line[3:].strip() 49 | 50 | github_root = "https://github.com/wzhe06/Reco-papers/blob/master/" 51 | all_dir = os.listdir("./") 52 | for one_dir in all_dir: 53 | if os.path.isdir(one_dir) and not one_dir.startswith('.'): 54 | out_file.write("\n### " + one_dir+"\n") 55 | if one_dir.strip() in paper_class_map: 56 | out_file.write(paper_class_map[one_dir.strip()] + "\n") 57 | all_sub_files = os.listdir(one_dir) 58 | for one_file in all_sub_files: 59 | if not os.path.isdir(one_file) and not one_file.startswith('.'): 60 | out_file.write("* [" + ('.').join(one_file.split('.')[:-1]) + "]("+github_root + urllib.quote(one_dir.strip())+"/" 61 | + urllib.quote(one_file.strip())+")
\n") 62 | if one_file.strip() in paper_map: 63 | out_file.write(paper_map[one_file.strip()] + "\n") 64 | 65 | out_file.close() 66 | --------------------------------------------------------------------------------