推荐系统

RecoSys

Recommend system learning resources and learning notes

目录

• 推荐系统论文
• 推荐系统学习笔记
• 资料积累
• 使用PyTorch实现DeepCTR模型

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

Deep Learning Recommender System

• [AutoRec] AutoRec: Autoencoders Meet Collaborative Filtering(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)

Embedding

• [Word2Vec] Distributed Representations of Words and Phrases and their Compositionality (Google 2013)
• [Word2Vec] Efficient Estimation of Word Representations in Vector Space (Google 2013)
• [Word2Vec] Word2vec Parameter Learning Explained (UMich 2016)
• [Item2Vec] Item2Vec-Neural Item Embedding for Collaborative Filtering (Microsoft 2016)

RecoSys_note

• 推荐系统实践-好的推荐系统
• 推荐系统实践-利用用户行为数据进行推荐
• 推荐系统实践-冷启动问题
• 推荐系统实践-利用标签数据进行推荐
• 推荐系统实践-利用时间上下文进行推荐
• 推荐系统实践-利用社交网络进行推荐
• 协同过滤与矩阵分解
• 自动特征交叉：FM-FFM
• 逻辑回归用于推荐系统
• 传统推荐模型的特点
• AutoRec-2015
• Deep Crossing-2016
• GBDT
• GBDT + LR

RecoSys_resource

传统机器学习推荐系统

• 机器学习中的MLE、MAP、贝叶斯估计
• 推荐系统召回四模型之：全能的FM模型
• 一篇文章搞定GBDT、Xgboost和LightGBM的面试
• 【机器学习】决策树（上）——ID3、C4.5、CART
• 机器学习】决策树（下）——XGBoost、LightGBM

推荐系统&深度学习网络

• 见微知著，你真的搞懂Google的Wide&Deep模型了吗？
• 详解 Wide & Deep 结构背后的动机

Embedding

• 无中生有：论推荐算法中的Embedding思想
• Embedding在深度推荐系统中的3大应用方向
• 一文梳理推荐系统的中Embedding的应用实践
• 深度学习中不得不学的Graph Embedding方法

DeepCTR_PyTorch

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