ReLLa: Retrieval-enhanced Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendation-Reference-Cited by-同舟云学术

ReLLa: Retrieval-enhanced Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendation

Published:2024-05-13 Issue: Volume:11 Page:3497-3508
ISSN:
Container-title:Proceedings of the ACM Web Conference 2024
language:
Short-container-title:

Author:

Lin Jianghao¹^ORCID,Shan Rong¹^ORCID,Zhu Chenxu²^ORCID,Du Kounianhua¹^ORCID,Chen Bo²^ORCID,Quan Shigang¹^ORCID,Tang Ruiming²^ORCID,Yu Yong¹^ORCID,Zhang Weinan¹^ORCID

Affiliation:

1. Shanghai Jiao Tong University, Shanghai, China

2. Huawei Noah's Ark Lab, Shenzhen, China

Funder

Shanghai Municipal Science and Technology Major Project

National Key R&D Program of China

National Natural Science Foundation of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3589334.3645467

Reference94 articles.

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2. David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver, and Colin A Rael. 2019. Mixmatch: A holistic approach to semi-supervised learning. Advances in neural information processing systems 32 (2019).

3. Vadim Borisov, Kathrin Seßler, Tobias Leemann, Martin Pawelczyk, and Gjergji Kasneci. 2022. Language models are realistic tabular data generators. arXiv preprint arXiv:2210.06280 (2022).

4. Tom Brown Benjamin Mann Nick Ryder Melanie Subbiah Jared D Kaplan Prafulla Dhariwal Arvind Neelakantan Pranav Shyam Girish Sastry Amanda Askell et al. 2020. Language models are few-shot learners. Advances in neural information processing systems 33 (2020) 1877--1901.

5. Aldo Gael Carranza, Rezsa Farahani, Natalia Ponomareva, Alex Kurakin, Matthew Jagielski, and Milad Nasr. 2023. Privacy-Preserving Recommender Systems with Synthetic Query Generation using Dierentially Private Large Language Models. arXiv preprint arXiv:2305.05973 (2023).

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