Joint Learning of Deep Retrieval Model and Product Quantization based Embedding Index

Ueber die erzeugung der invarianten durch integration

Adolf Hurwitz. 1963 · 1963

Earlier work this paper cites.

A generalized solution of the orthogonal procrustes problem

Peter Schönemann. 1966 · 1966

Earlier work this paper cites.

Matrix Computations (3rd Ed.)

Gene H. Golub and Charles F. Van Loan. 1996 · 1996

Earlier work this paper cites.

Locality-sensitive hashing scheme based on p-stable distributions. In Proceedings of the twentieth annual symposium on Computational geometry . 253–262

Mayur Datar, Nicole Immorlica, Piotr Indyk, and Vahab S Mirrokni. 2004 · 2004

Earlier work this paper cites.

Visualizing Data using t-SNE

Laurens van der Maaten and Geoffrey Hinton. 2008 · 2008

Earlier work this paper cites.

Challenges in building large-scale information retrieval systems. In WSDM , Vol. 10

Jeffrey Dean. 2009 · 2009

Earlier work this paper cites.

Product quantization for nearest neighbor search

Herve Jegou, Matthijs Douze, and Cordelia Schmid. 2010 · 2010

Earlier work this paper cites.

Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation. In arXiv . 1308.3432

Yoshua Bengio, Nicholas Léonard, and Aaron Courville. 2013 · 2013

Earlier work this paper cites.

Optimized product quantization for approximate nearest neighbor search. In CVPR . 2946–2953

Tiezheng Ge, Kaiming He, Qifa Ke, and Jian Sun. 2013 · 2013

Earlier work this paper cites.

Learning deep structured semantic models for web search using clickthrough data. In CIKM . 2333–2338

Po-Sen Huang, Xiaodong He, Jianfeng Gao, Li Deng, Alex Acero, and Larry Heck. 2013 · 2013

Earlier work this paper cites.

The movielens datasets: History and context

F Maxwell Harper and Joseph A Konstan. 2015 · 2015

Cited alongside, same era.

Coordinate descent algorithms

Stephen J Wright. 2015 · 2015

Cited alongside, same era.

Deep quantization network for efficient image retrieval. In AAAI , Vol. 30

Yue Cao, Mingsheng Long, Jianmin Wang, Han Zhu, and Qingfu Wen. 2016 · 2016

Cited alongside, same era.

Deep Neural Networks for YouTube Recommendations. In RecSys . 191–198

Paul Covington, Jay Adams, and Emre Sargin. 2016 · 2016

Cited alongside, same era.

Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering. In WWW . 507–517

Ruining He and Julian McAuley. 2016 · 2016

Cited alongside, same era.

In defense of product quantization

Original

Learning tree-based deep model for recommender systems. In SIGKDD . 1079–1088

Han Zhu, Xiang Li, Pengye Zhang, Guozheng Li, Jie He, Han Li, and Kun Gai. 2018 · 2018

Later among the works it cites.

Billion-scale similarity search with GPUs

Jeff Johnson, Matthijs Douze, and Hervé Jégou. 2019 · 2019

Later among the works it cites.

Multi-Interest Network with Dynamic Routing for Recommendation at Tmall. In CIKM . 2615–2623

Chao Li, Zhiyuan Liu, Mengmeng Wu, Yuchi Xu, Huan Zhao, Pipei Huang, Guoliang Kang, Qiwei Chen, Wei Li, and Dik Lun Lee. 2019 · 2019

Later among the works it cites.

Differentiable product quantization for end-to-end embedding compression. In ICML . 1617–1626

Ting Chen, Lala Li, and Yizhou Sun. 2020 · 2020

Later among the works it cites.

Accelerating large-scale inference with anisotropic vector quantization. In ICML . 3887–3896

Ruiqi Guo, Philip Sun, Erik Lindgren, Quan Geng, David Simcha, Felix Chern, and Sanjiv Kumar. 2020 · 2020

Later among the works it cites.