1.學習之路
1.1.基礎階段
參考資料：[1]《推薦系統實踐》，項亮；[2]《推薦系統開發實戰》，高陽團；[3]《美團機器學習實踐》，美團算法團隊；[4]《計算廣告》，劉鵬。注意：[3]和[4]更多的偏重概念，代碼不多。
1.2.進階之路
主要參考知乎回答：想學習推薦系統，如何從小白成爲高手?第一個回答者"劉十三"，整理的資料很不錯，嘿嘿嘿~
1.2.1.推薦系統/計算廣告/機器學習/CTR預估資料彙總
github鏈接：https://github.com/AllenZqy/RecSys
1.2.2.推薦/搜索/廣告 精選文章
github鏈接：https://github.com/AllenZqy/RecNews
1.2.3.經典論文
作者：石曉文
鏈接：https://www.zhihu.com/question/23194692/answer/805896718
來源：知乎
著作權歸作者所有。商業轉載請聯繫作者獲得授權，非商業轉載請註明出處。
FM：《Factorization Machines》
FFM：《Field-aware Factorization Machines for CTR Prediction》
DeepFM：《DeepFM: A Factorization-Machine based Neural Network for CTR Prediction》Wide & Deep：《Wide & Deep Learning for Recommender Systems》
DCN：《Deep & Cross Network for Ad Click Predictions》
NFM：《Neural Factorization Machines for Sparse Predictive Analytics》
AFM：《Attentional Factorization Machines:Learning the Weight of Feature Interactions via Attention Networks》
GBDT + LR：《Practical Lessons from Predicting Clicks on Ads at Facebook》
MLR：《Learning Piece-wise Linear Modelsfrom Large Scale Data for Ad Click Prediction》
DIN：《Deep Interest Network for Click-Through Rate Prediction》
DIEN：《Deep Interest Evolution Network for Click-Through Rate Prediction》
BPR：《BPR: Bayesian Personalized Ranking from Implicit Feedback》
Youtube：《Deep Neural Networks for YouTube Recommendations》
1.2.4.持續跟進最新論文
作者：石曉文
鏈接：https://www.zhihu.com/question/23194692/answer/805896718
來源：知乎
著作權歸作者所有。商業轉載請聯繫作者獲得授權，非商業轉載請註明出處。
在不斷跟進推薦系統論文的過程中，你會發現推薦系統會借鑑各個領域的方法， 持續跟進最近推薦論文，對我們學習其他領域如NLP、圖像領域、強化學習等等都會有所幫助。接下來列舉一些借鑑其他領域方法的一些文章吧，也算是對第三部分的一個補充。
強化學習
[1]《DRN: A Deep Reinforcement Learning Framework for News Recommendation》
[2]《Deep Reinforcement Learning for List-wise Recommendations》
多任務學習
[1]《Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate》
[2]《Why I like it: Multi-task Learning for Recommendation and Explanation》
GAN
[1]《IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models》
[2]《CFGAN: A Generic Collaborative Filtering Framework based on Generative Adversarial Networks》
知識圖譜
[1]《DKN: Deep Knowledge-Aware Network for News Recommendation》
[2]《RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems》
[3]《Multi-task Learning for KG enhanced Recommendation》
[4]《Perceive Your Users in Depth: Learning Universal User Representations from Multiple E-commerce Tasks》
Transformer
[1]《Next Item Recommendation with Self-Attention》
[2]《Deep Session Interest Network for Click-Through Rate Prediction》
[3]《Behavior Sequence Transformer for E-commerce Recommendation in Alibaba》
[4]《BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer》
RNN & GNN
[1]《SESSION-BASED RECOMMENDATIONS WITH RECURRENT NEURAL NETWORKS》
[2]《Improved Recurrent Neural Networks for Session-based Recommendations》
[3]《Session-based Recommendation with Graph Neural Networks》
Embedding技巧
[1]《Real-time Personalization using Embeddings for Search Ranking at Airbnb》
[2]《Learning and Transferring IDs Representation in E-commerce》
[3]《Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba》