QARM: Quantitative Alignment Multi-Modal Recommendation at Kuaishou

Vector quantization

Robert Gray. 1984 · 1984

Earlier work this paper cites.

Factorization machines. In IEEE International Conference on Data Mining (ICDM)

Steffen Rendle. 2010 · 2010

Earlier work this paper cites.

Sparse representation for computer vision and pattern recognition

John Wright, Yi Ma, Julien Mairal, Guillermo Sapiro, Thomas S Huang, and Shuicheng Yan. 2010 · 2010

Earlier work this paper cites.

Additive quantization for extreme vector compression. In IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR)

Artem Babenko and Victor Lempitsky. 2014 · 2014

Earlier work this paper cites.

Stacked quantizers for compositional vector compression

Julieta Martinez, Holger H Hoos, and James J Little. 2014 · 2014

Earlier work this paper cites.

Deep Neural Networks for YouTube Recommendations. In ACM Conference on Recommender Systems (RecSys)

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

Earlier work this paper cites.

Regularized Residual Quantization: a multi-layer sparse dictionary learning approach

Sohrab Ferdowsi, Slava Voloshynovskiy, and Dimche Kostadinov. 2017 · 2017

Earlier work this paper cites.

Visually-Aware Fashion Recommendation and Design with Generative Image Models. In IEEE International Conference on Data Mining (ICDM)

Wang-Cheng Kang, Chen Fang, Zhaowen Wang, and Julian McAuley. 2017 · 2017

Earlier work this paper cites.

Performance guaranteed network acceleration via high-order residual quantization. In International Conference on Computer Vision (ICCV)

Zefan Li, Bingbing Ni, Wenjun Zhang, Xiaokang Yang, and Wen Gao. 2017 · 2017

Earlier work this paper cites.

Neural discrete representation learning

Aaron Van Den Oord, Oriol Vinyals, et al · 2017

Earlier work this paper cites.

Deep Interest Network for Click-Through Rate Prediction. In ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)

Guorui Zhou, Xiaoqiang Zhu, Chenru Song, Ying Fan, Han Zhu, Xiao Ma, Yanghui Yan, Junqi Jin, Han Li, and Kun Gai. 2018 · 2018

Earlier work this paper cites.

Deep Learning Recommendation Model for Personalization and Recommendation Systems

Maxim Naumov, Dheevatsa Mudigere, Hao-Jun Michael Shi, Jianyu Huang, Narayanan Sundaraman, Jongsoo Park, Xiaodong Wang, Udit Gupta, Carole-Jean Wu, Alisson G. Azzolini, Dmytro Dzhulgakov, Andrey Mallevich, Ilia Cherniavskii, Yinghai Lu, Raghuraman Krishnamoorthi, Ansha Yu, Volodymyr Kondratenko, Stephanie Pereira, Xianjie Chen, Wenlin Chen, Vijay Rao, Bill Jia, Liang Xiong, and Misha Smelyanskiy. 2019 · 2019

Earlier work this paper cites.

Deep Interest Evolution Network for Click-Through Rate Prediction. In AAAI Conference on Artificial Intelligence (AAAI)

Guorui Zhou, Na Mou, Ying Fan, Qi Pi, Weijie Bian, Chang Zhou, Xiaoqiang Zhu, and Kun Gai. 2019 · 2019

Cited alongside, same era.

Language models are few-shot learners

Tom B Brown. 2020 · 2020

Cited alongside, same era.

Mining of massive data sets

Jure Leskovec, Anand Rajaraman, and Jeffrey David Ullman. 2020 · 2020

Cited alongside, same era.

Large scale product graph construction for recommendation in e-commerce

Xiaoyong Yang, Yadong Zhu, Yi Zhang, Xiaobo Wang, and Quan Yuan. 2020 · 2020

Cited alongside, same era.

Taming transformers for high-resolution image synthesis. In IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR)

Patrick Esser, Robin Rombach, and Bjorn Ommer. 2021 · 2021

Cited alongside, same era.

TWIN: TWo-stage Interest Network for Lifelong User Behavior Modeling in CTR Prediction at Kuaishou. In ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)

Jianxin Chang, Chenbin Zhang, Zhiyi Fu, Xiaoxue Zang, Lin Guan, Jing Lu, Yiqun Hui, Dewei Leng, Yanan Niu, Yang Song, and Kun Gai. 2023 · 2023

Later among the works it cites.

Learning vector-quantized item representation for transferable sequential recommenders. In International World Wide Web Conference (WWW)

Yupeng Hou, Zhankui He, Julian McAuley, and Wayne Xin Zhao. 2023 · 2023

Later among the works it cites.

BLIP-2: bootstrapping language-image pre-training with frozen image encoders and large language models. In International Conference on Machine Learning (ICML)

Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. 2023 · 2023

Later among the works it cites.

Recommender systems with generative retrieval

Shashank Rajput, Nikhil Mehta, Anima Singh, Raghunandan Hulikal Keshavan, Trung Vu, Lukasz Heldt, Lichan Hong, Yi Tay, Vinh Tran, Jonah Samost, et al · 2023

Later among the works it cites.