RecoSys

Reco_Notes

Personal learning resources and learning notes

• RecoSys_Paper
• RecoSys_Note
• RecoSys_DNN
• RescoSys_BigData
• RescoSys_Project

RecoSys_Paper

Classic Recommender System

• [Earliest CF] Using Collaborative Filtering to Weave an Information Tapestry (PARC 1992)
• [ItemCF] Item-Based Collaborative Filtering Recommendation Algorithms (UMN 2001)
• [CF] Amazon Recommendations Item-to-Item Collaborative Filtering (Amazon 2003)
• [Bilinear] Personalized Recommendation on Dynamic Content Using Predictive Bilinear Models (Yahoo 2009)
• [MF] Matrix Factorization Techniques for Recommender Systems (Yahoo 2009)
• [FM]Factorization Machines2010
• [Recsys Intro] Recommender Systems Handbook (FRicci 2011)
• [Recsys Intro slides] Recommender Systems An introduction (DJannach 2014)
• [GBDT+LR](Practical Lessons from Predicting Clicks on Ads at Facebook 2014)

Deep Learning Recommender System

• [AutoRec] AutoRec: Autoencoders Meet Collaborative Filtering(2015)
• [DSSM in Recsys] A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems (Microsoft 2015)
• [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)
• [Wide&Deep] Wide & Deep Learning for Recommender Systems (Google 2016)
• [FNN] Deep Learning over Multi-field Categorical Data (UCL 2016)
• [NCF] Neural Collaborative Filtering (NUS 2017)
• [DCN] Deep & Cross Network for Ad Click Predictions (Stanford 2017)
• [DeepFM] A Factorization-Machine based Neural Network for CTR Prediction (HIT-Huawei 2017)
• [AFM] Attentional Factorization Machines - Learning the Weight of Feature Interactions via Attention Networks (ZJU 2017)
• [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)
• [xDeepFM] xDeepFM - Combining Explicit and Implicit Feature Interactions for Recommender Systems (USTC 2018)

Embedding

• [LSH] Locality-Sensitive Hashing for Finding Nearest Neighbors (IEEE 2008)
• [Word2Vec] Distributed Representations of Words and Phrases and their Compositionality (Google 2013)
• [Word2Vec] Efficient Estimation of Word Representations in Vector Space (Google 2013)
• [Graph Embedding] DeepWalk- Online Learning of Social Representations (SBU 2014)
• [Node2vec] Node2vec - Scalable Feature Learning for Networks (Stanford 2016)
• [Word2Vec] Word2vec Parameter Learning Explained (UMich 2016)
• [Item2Vec] Item2Vec-Neural Item Embedding for Collaborative Filtering (Microsoft 2016)
• [SDNE] Structural Deep Network Embedding (THU 2016)
• [Airbnb Embedding] Real-time Personalization using Embeddings for Search Ranking at Airbnb (Airbnb 2018)
• [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)

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)

RecoSys_note

• Graph_Embedding

RecoSys_DNN_PyTorch_TF

• CollaborativeFiltering
• AutoRec-2015
• Deep Crossing-2016
• NeuralCF-2016
• AFM-2017