.
├── ./Classic Recommender System
│ ├── ./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
│ ├── ./Classic Recommender System/[Earliest CF] Using Collaborative Filtering to Weave an Information Tapestry (PARC 1992).pdf
│ ├── ./Classic Recommender System/[ItemCF] Item-Based Collaborative Filtering Recommendation Algorithms (UMN 2001).pdf
│ ├── ./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
│ └── ./Classic Recommender System/[Recsys Intro] Recommender Systems Handbook (FRicci 2011).pdf
├── ./Deep Learning Recommender System
│ ├── ./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
│ ├── ./Deep Learning Recommender System/[DCN] Deep & Cross Network for Ad Click Predictions (Stanford 2017).pdf
│ ├── ./Deep Learning Recommender System/[DIEN] Deep Interest Evolution Network for Click-Through Rate Prediction (Alibaba 2019).pdf
│ ├── ./Deep Learning Recommender System/[DIN] Deep Interest Network for Click-Through Rate Prediction (Alibaba 2018).pdf
│ ├── ./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
│ ├── ./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
│ ├── ./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
│ ├── ./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
│ ├── ./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
│ ├── ./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
│ ├── ./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
├── ./Embedding
│ ├── ./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
│ ├── ./Embedding/[Graph Embedding] DeepWalk- Online Learning of Social Representations (SBU 2014).pdf
│ ├── ./Embedding/[Item2Vec] Item2Vec-Neural Item Embedding for Collaborative Filtering (Microsoft 2016).pdf
│ ├── ./Embedding/[LINE] LINE - Large-scale Information Network Embedding (MSRA 2015).pdf
│ ├── ./Embedding/[LSH] Locality-Sensitive Hashing for Finding Nearest Neighbors (IEEE 2008).pdf
│ ├── ./Embedding/[Negative Sampling] Word2vec Explained Negative-Sampling Word-Embedding Method (2014).pdf
│ ├── ./Embedding/[Node2vec] Node2vec - Scalable Feature Learning for Networks (Stanford 2016).pdf
│ ├── ./Embedding/[SDNE] Structural Deep Network Embedding (THU 2016).pdf
│ ├── ./Embedding/[Word2Vec] Distributed Representations of Words and Phrases and their Compositionality (Google 2013).pdf
│ ├── ./Embedding/[Word2Vec] Efficient Estimation of Word Representations in Vector Space (Google 2013).pdf
│ └── ./Embedding/[Word2Vec] Word2vec Parameter Learning Explained (UMich 2016).pdf
├── ./Evaluation
│ ├── ./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
│ ├── ./Evaluation/[EE Evaluation Intro] Offline Evaluation and Optimization for Interactive Systems (Microsoft 2015).pdf
│ ├── ./Evaluation/[InterLeaving] Large-Scale Validation and Analysis of Interleaved Search Evaluation (Yahoo 2012).pdf
│ └── ./Evaluation/[Replay] Unbiased Offline Evaluation of Contextual-bandit-based News Article Recommendation Algorithms (Yahoo 2012).pdf
├── ./Exploration and Exploitation
│ ├── ./Exploration and Exploitation/[EE Intro] Exploration and Exploitation Problem Introduction by Wang Zhe (Hulu 2017).pdf
│ ├── ./Exploration and Exploitation/[EE in Ads] Customer Acquisition via Display Advertising Using MultiArmed Bandit Experiments (UMich 2015).pdf
│ ├── ./Exploration and Exploitation/[EE in Ads] Exploitation and Exploration in a Performance based Contextual Advertising System (Yahoo 2010).pdf
│ ├── ./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
│ ├── ./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
│ ├── ./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
│ ├── ./Exploration and Exploitation/[UCB1] Bandit Algorithms Continued - UCB1 (Noel Welsh 2010).pdf
│ └── ./Exploration and Exploitation/[UCT] Exploration exploitation in Go UCT for Monte-Carlo Go (UPSUD 2016).pdf
├── ./Famous Machine Learning Papers
│ ├── ./Famous Machine Learning Papers/[CNN] ImageNet Classification with Deep Convolutional Neural Networks (UofT 2012).pdf
│ └── ./Famous Machine Learning Papers/[RNN] Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation (UofM 2014).pdf
├── ./Industry Recommender System
│ ├── ./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
│ ├── ./Industry Recommender System/[Baidu slides] DNN in Baidu Ads (Baidu 2017).pdf
│ ├── ./Industry Recommender System/[Netflix] The Netflix Recommender System- Algorithms, Business Value, and Innovation (Netflix 2015).pdf
│ ├── ./Industry Recommender System/[Pinterest] Graph Convolutional Neural Networks for Web-Scale Recommender Systems (Pinterest 2018).pdf
│ ├── ./Industry Recommender System/[Pinterest] Personalized content blending In the Pinterest home feed (Pinterest 2016).pdf
│ ├── ./Industry Recommender System/[Quora] Building a Machine Learning Platform at Quora (Quora 2016).pdf
│ └── ./Industry Recommender System/[Youtube] Deep Neural Networks for YouTube Recommendations (Youtube 2016).pdf
├── ./LICENSE
├── ./README.md
├── ./Reco-papers.txt
├── ./Reinforcement Learning in Reco
│ ├── ./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
│ ├── ./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
├── ./_config.yml
└── ./generateReadme.py
8 directories, 73 files
原文：https://www.cnblogs.com/cx2016/p/13836707.html