Reco_Notes
Recommend system learning resources and learning notes
目录
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)
-[Airbnb Embedding] Real-time Personalization using Embeddings for Search Ranking at Airbnb (Airbnb 2018).pdf)
RecoSys_note
- 《推荐系统实践》- 什么是好的推荐系统—%20一个好的推荐系统.md)
- 《推荐系统实践》- 利用用户行为数据进行推荐—利用用户行为数据进行推荐.md)
- 《推荐系统实践》- 推荐系统的冷启动问题—冷启动问题.md)
- 《推荐系统实践》- 利用标签数据进行推荐—利用标签数据进行推荐.md)
- 《推荐系统实践》- 利用时间上下文进行推荐-利用上下文信息进行推荐.md)
- 《推荐系统实践》- 利用社交网络进行推荐—利用社交网络推荐.md)
- 协同过滤与矩阵分解
- 自动特征交叉：FM-FFM
- 逻辑回归与推荐系统
- 传统推荐模型的特点
- AutoRec-2015论文笔记
- Deep Crossing-2016论文笔记
- NeuralCF-2017论文笔记
- GBDT原理总结
- GBDT + LR
- 推荐系统中的特征工程
- Spark与特征处理
- Embedding基础
- 初识Graph Embedding
- 线上服务及存储模块
- 召回层中的常用策略
- Embedding召回中的局部敏感哈希策略
- 基于Embedding+MLP的Deep Crossing
- 综合“记忆”和“泛化”的Wide&Deep
- 协同过滤的深度学习进化:NeuralCF
- 基于FM与Wide&Deep的DeepFM
- 基于Attention的推荐系统-DIN和DIEN
- 常用离线评估方法
- 常用离线评估指标
- 理解线上A/B测试
- 推荐系统中的的评估体系
- YouTobe推荐系统架构
RecoSys_resource
RescoSys_ML_DL
- 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
- 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