awesome-deep-learning-papers-for-search-recommendation-advertising
github.com/guyulongcs/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, Post Ranking, Relevance, LLM and RL. Please cite our paper "Deep Learning to Rank in Industrial Search Engines, Recommender Systems, and Online Advertising - An Overview and New Perspectives" (TOIS 2026).
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Add the Context Awesome MCP server to Claude, Cursor, or any MCP client, then ask:
"Show me 09_reinforcement_learning resources from awesome-deep-learning-papers-for-search-recommendation-advertising"
Installation instructions →What's inside
09_Reinforcement_Learning
- 1992 (ML) [REINFORCE] Simple statistical gradient-following algorithms for connectionist reinforcement learning
- 1999 (NIPS) [Actor-Critic] Actor-Critic Algorithms
- 2010 (Yahoo) (WWW) [LinUCB] A Contextual-Bandit Approach to Personalized News Article Recommendation
- 2013 (DeepMind) (Arxiv) [DQN] Playing Atari with Deep Reinforcement Learning
- 2015 (DeepMind) (AAAI) [Double Q-learning] Deep Reinforcement Learning with Double Q-learning
- 2015 (DeepMind) (Nature) [DQN] Human-level control through deep reinforcement learning
02_Matching
- 1994 (CSCW) [User-CF] GroupLens - An Open Architecture for Collaborative Filtering of Netnews
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
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
05_Post-ranking
- 1998 (SIGIR) ** [MRR] The Use of MMR, Diversity-Based Reranking for Reordering Documents and Producing Summaries
- 2005 (WWW) Improving Recommendation Lists Through Topic Diversification
- 2008 (SIGIR) [α-NDCG] Novelty and Diversity in Information Retrieval Evaluation
- 2009 (Microsoft) (WSDM) Diversifying Search Results
- 2010 (WWW) Exploiting Query Reformulations for Web Search Result Diversification
- 2015 (Google) (Arxiv) Deep Reinforcement Learning in Large Discrete Action Spaces
04_Ranking
- 2008 (KDD) Learning Classifiers from Only Positive and Unlabeled Data
- 2010 (Google) Overlapping Experiment Infrastructure - More, Better, Faster Experimentation
More, Better, Faster Experimentation
- 2010 (ICDM) [FM] Factorization machines
- 2013 (Google) (KDD) [LR] Ad Click Prediction - a View from the Trenches
a View from the Trenches
- 2014 (ADKDD) (Facebook) Practical Lessons from Predicting Clicks on Ads at Facebook
- 2014 (ADKDD) (Facebook) Practical Lessons from Predicting Clicks on Ads at Facebook
08_Deep_Learning
- 2012 (NIPS) [CNN] ImageNet Classification with Deep Convolutional Neural Networks
- 2014 (JMLR) [Dropout] Dropout - A Simple Way to Prevent Neural Networks from Overfitting
A Simple Way to Prevent Neural Networks from Overfitting
- 2015 (Google) (JMLR) [BatchNorm] Batch Normalization - Accelerating Deep Network Training by Reducing Internal Covariate Shift
Accelerating Deep Network Training by Reducing Internal Covariate Shift
- 2015 (OpenAI) (ICLR) [Adam] Adam - A Method for Stochastic Optimization
A Method for Stochastic Optimization
- 2016 (CVPR) [ResNet] Deep Residual Learning for Image Recognition
- 2016 (OpenAI) (NIPS) [Weight Norm] Weight Normalization - A Simple Reparameterization to Accelerate Training of Deep Neural Networks
A Simple Reparameterization to Accelerate Training of Deep Neural Networks
07_LLM
- 2013 (Google) (NIPS) [Word2vec] Distributed Representations of Words and Phrases and their Compositionality
- 2014 (Google) (NIPS) [Seq2Seq] Sequence to Sequence Learning with Neural Networks
- 2017 (Google) (NIPS) [Transformer] Attention Is All You Need
- 2017 (OpenAI) (NIPS) [RLHF] Deep Reinforcement Learning from Human Preferences
01_Embedding
- 2013 (Google) (NIPS) [Word2vec] Distributed Representations of Words and Phrases and their Compositionality
- 2014 (KDD) [DeepWalk] DeepWalk - online learning of social representations
online learning of social representations
- 2015 (WWW) [LINE] LINE Large-scale Information Network Embedding
- 2016 (KDD) [Node2vec] node2vec - Scalable Feature Learning for Networks
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
Learning Node Representations from Structural Identity
Showing a sample of 525 resources. View the full list on GitHub →