RecoSys
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)
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