Learning Vector-Quantized Item Representation for Transferable Sequential Recommenders-Reference-Cited by-同舟云学术

Learning Vector-Quantized Item Representation for Transferable Sequential Recommenders

Published:2023-04-30 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the ACM Web Conference 2023
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Author:

Hou Yupeng¹^ORCID,He Zhankui²^ORCID,McAuley Julian²^ORCID,Zhao Wayne Xin³^ORCID

Affiliation:

1. Renmin University of China, China

2. UC San Diego, USA

3. Beijing Key Laboratory of Big Data Management and Analysis Methods, Renmin University of China, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3543507.3583434

Reference74 articles.

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2. Tom Brown Benjamin Mann Nick Ryder Melanie Subbiah Jared D Kaplan Prafulla Dhariwal Arvind Neelakantan Pranav Shyam Girish Sastry Amanda Askell 2020. Language models are few-shot learners. In NeurIPS. Tom Brown Benjamin Mann Nick Ryder Melanie Subbiah Jared D Kaplan Prafulla Dhariwal Arvind Neelakantan Pranav Shyam Girish Sastry Amanda Askell 2020. Language models are few-shot learners. In NeurIPS.

3. Jianxin Chang Chen Gao Yu Zheng Yiqun Hui Yanan Niu Yang Song Depeng Jin and Yong Li. 2021. Sequential Recommendation with Graph Neural Networks. In SIGIR. Jianxin Chang Chen Gao Yu Zheng Yiqun Hui Yanan Niu Yang Song Depeng Jin and Yong Li. 2021. Sequential Recommendation with Graph Neural Networks. In SIGIR.

4. Ting Chen Simon Kornblith Mohammad Norouzi and Geoffrey Hinton. 2020. A simple framework for contrastive learning of visual representations. In ICML. Ting Chen Simon Kornblith Mohammad Norouzi and Geoffrey Hinton. 2020. A simple framework for contrastive learning of visual representations. In ICML.

5. Ting Chen Lala Li and Yizhou Sun. 2020. Differentiable product quantization for end-to-end embedding compression. In ICML. Ting Chen Lala Li and Yizhou Sun. 2020. Differentiable product quantization for end-to-end embedding compression. In ICML.

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