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.
- 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
- 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