本文来自“深度推荐系统”专栏，这个系列将介绍在深度学习的强力驱动下，给推荐系统工业界所带来的最前沿的变化。本文主要根据Google推出的引领推荐系统与CTR预估工业界潮流至今的一大神作W&D[1]，所总结出来的深度推荐系统与CTR预估工业界必读的论文汇总。

起初是因为在唐杰老师的微博上看到其学生整理的一个关于Bert论文高引用相关的图片（https://weibo.com/2126427211/I4cXHxIy4）。

一个伟大的学生做的一个BERT的论文以及它引用的文章之间的关系，相当于是一个针对论文Citation的Finding->Reasoning->Exploring的过程。感觉做得很酷，忍不住share出来了。。。他伟大地决定要写个算法自动搞定！

觉得这个整理思路不错，于是也照葫芦画瓢整理了一下推荐系统和CTR预估上工业界同样鼎鼎大名Google出品的W&D[1]论文相关高引用的论文汇总。其实主要是对近年来推荐系统和CTR预估工业界影响力较大的论文做一个简单的思路梳理，首先上图如下，圆圈内数字为论文被引用数量。

Collaborative Filtering

Deep部分演进

[SIGIR 17] Neural Factorization Machines for Sparse Predictive Analytics
[IJCAI 17] Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks
[ECIR 16] Factorization-supported Neural Network
[TOIS 18] Product-Based Neural Networks for User Response Prediction over Multi-Field Categorical Data
[RecSys 19] FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction
[KDD 18] Deep Interest Network for Click-through Rate Prediction
[AAAI 19] Deep Interest Evolution Network for Click-Through Rate Prediction
[IJCAI 19] Deep Session Interest Network for Click-Through Rate Prediction
[CIKM 19] AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks

Wide部分演进

[IJCAI 17] DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
[KDD 17] Deep & Cross Network for Ad Click Predictions
[KDD 18] xDeepFM: Combining Explicit and Implicit Feature Interactions
for Recommender Systems
[WWW 19] Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction

强化学习

[WWW 17] DRN: A Deep Reinforcement Learning Framework for News Recommendation
[WSDM 19] Top-K Off-Policy Correction for a REINFORCE Recommender System
[IJCAI 19] Reinforcement Learning for Slate-based Recommender Systems: A Tractable Decomposition and Practical Methodology

知识图谱

[WWW 17] DKN: Deep Knowledge-Aware Network for News Recommendation
[CIKM 18] RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems

Embedding技术

[ICCCA 18] Item2Vec-Neural Item Embedding for Collaborative Filtering
[RecSys 16] Meta-Prod2Vec: Product Embeddings Using Side-Information for Recommendation
[KDD 18] Real-time Personalization using Embeddings for Search Ranking at Airbnb
[KDD 18] Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba
[WWW 19] NetSMF: Large-Scale Network Embedding as Sparse Matrix Factorization
[IJCAI 19] ProNE: Fast and Scalable Network Representation Learning

参考文献

本文授权转载自知乎专栏“深度推荐系统”。原文链接：https://zhuanlan.zhihu.com/p/73369087

经典推荐：工业界深度推荐系统与 CTR 预估方向必读论文汇总