Awesome-Deep-Learning-Papers-for-Search-Recommendation-Advertising

Awesome Deep Learning papers for industrial Search, Recommendation and Advertisement. They focus on Embedding, Matching, Pre-Ranking, Ranking (CTR/CVR prediction), Post-Ranking, Relevance-Ranking, LLM based ranking, Reinforcement Learning and so on.

Please cite our paper: Deep Learning to Rank in Industrial Search Engines, Recommender Systems, and Online Advertising - An Overview and New Perspectives, which was published in TOIS 2026.

00_Overview

• 2026 (TOIS) [DLTR] Deep Learning to Rank in Industrial Search Engines, Recommender Systems, and Online Advertising - An Overview and New Perspectives
  

01_Embedding

• 2013 (Google) (NIPS) [Word2vec] Distributed Representations of Words and Phrases and their Compositionality
  
• 2014 (KDD) [DeepWalk] DeepWalk - online learning of social representations
  
• 2015 (WWW) [LINE] LINE Large-scale Information Network Embedding
  
• 2016 (KDD) [Node2vec] node2vec - Scalable Feature Learning for Networks
  
• 2017 (ICLR) [GCN] Semi-supervised Classification with Graph Convolutional Networks
  
• 2017 (KDD) [Struc2vec] struc2vec - Learning Node Representations from Structural Identity
  
• 2017 (NIPS) [GraphSAGE] Inductive Representation Learning on Large Graphs
  
• 2018 (Alibaba) (KDD) *[Alibaba Embedding] Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba
  
• 2018 (ICLR) [GAT] Graph Attention Networks
  
• 2018 (Pinterest) (KDD) *[PinSage] Graph Convolutional Neural Networks for Web-Scale Recommender Systems
  
• 2018 (WSDM) [NetMF] Network embedding as matrix factorization - Unifying deepwalk, line, pte, and node2vec
  
• 2019 (Alibaba) (KDD) *[GATNE] Representation Learning for Attributed Multiplex Heterogeneous Network
  

02_Matching

• 1994 (CSCW) [User-CF] GroupLens - An Open Architecture for Collaborative Filtering of Netnews
  
• 1998 (Microsoft) Empirical Analysis of Predictive Algorithms for Collaborative Filtering
  
• 2003 (Amazon) [Item-CF] Amazon.com recommendations - item-to-item collaborative filtering
  
• 2009 (Computer) [MF] Matrix factorization techniques for recommender systems
  
• 2013 (Microsoft) (CIKM) [DSSM] Learning deep structured semantic models for web search using clickthrough data
  
• 2015 (KDD) [Sceptre] Inferring Networks of Substitutable and Complementary Products
  
• 2016 (Google) (RecSys) **[Youtube DNN] Deep Neural Networks for YouTube Recommendations
  
• 2018 (Airbnb) (KDD) *[Airbnb Embedding] Real-time Personalization using Embeddings for Search Ranking at Airbnb
  
• 2018 (Alibaba) (KDD) * [TDM] Learning Tree-based Deep Model for Recommender Systems
  
• 2018 (Pinterest) (KDD) *[PinSage] Graph Convolutional Neural Networks for Web-Scale Recommender Systems
  
• 2019 (Alibaba) (CIKM) **[MIND] Multi-Interest Network with Dynamic Routing for Recommendation at Tmall
  
• 2019 (Alibaba) (CIKM) *[SDM] SDM - Sequential deep matching model for online large-scale recommender system
  
• 2019 (Alibaba) (NIPS) *[JTM] Joint Optimization of Tree-based Index and Deep Model for Recommender Systems
  
• 2019 (Amazon) (KDD) Semantic Product Search
  
• 2019 (Baidu) (KDD) *[MOBIUS] MOBIUS - Towards the Next Generation of Query-Ad Matching in Baidu's Sponsored Search
  
• 2019 (Google) (RecSys) **[Two-Tower] Sampling-Bias-Corrected Neural Modeling for Large Corpus Item Recommendations
  
• 2019 (Google) (WSDM) *[Top-K Off-Policy] Top-K Off-Policy Correction for a REINFORCE Recommender System
  
• 2019 [Tencent] (KDD) A User-Centered Concept Mining System for Query and Document Understanding at Tencent
  
• 2020 (Alibaba) (Arxiv) [SWING] Large Scale Product Graph Construction for Recommendation in E-commerce
  
• 2020 (Alibaba) (ICML) [OTM] Learning Optimal Tree Models under Beam Search
  
• [2020