Data Augmentation for Sequential Recommendation: A Survey

Inferring networks of substitutable and complementary products

Julian McAuley, Rahul Pandey, and Jure Leskovec · 2015

Earlier work this paper cites.

Improved recurrent neural networks for session-based recommendations

Yong Kiam Tan, Xinxing Xu, and Yong Liu · 2016

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Data augmentation instead of explicit regularization

Alex Hernández-García and Peter König · 2018

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Self-attentive sequential recommendation

Wang-Cheng Kang and Julian McAuley · 2018

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Personalized top-n sequential recommendation via convolutional sequence embedding

Jiaxi Tang and Ke Wang · 2018

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Bert4rec: Sequential recommendation with bidirectional encoder representations from transformer

Fei Sun, Jun Liu, Jian Wu, Changhua Pei, Xiao Lin, Wenwu Ou, and Peng Jiang · 2019

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A survey of data augmentation approaches for nlp

Steven Y Feng, Varun Gangal, Jason Wei, Sarath Chandar, Soroush Vosoughi, Teruko Mitamura, and Eduard Hovy · 2021

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Sequential recommendation with bidirectional chronological augmentation of transformer

Juyong Jiang, Yingtao Luo, Jae Boum Kim, Kai Zhang, and Sunghun Kim · 2021

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Contrastive self-supervised sequential recommendation with robust augmentation

Zhiwei Liu, Yongjun Chen, Jia Li, Philip S Yu, Julian McAuley, and Caiming Xiong · 2021

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Augmenting sequential recommendation with pseudo-prior items via reversely pre-training transformer

Zhiwei Liu, Ziwei Fan, Yu Wang, and Philip S Yu · 2021

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Counterfactual data-augmented sequential recommendation

Zhenlei Wang, Jingsen Zhang, Hongteng Xu, Xu Chen, Yongfeng Zhang, Wayne Xin Zhao, and Ji-Rong Wen · 2021

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Fedgnn: Federated graph neural network for privacy-preserving recommendation

Chuhan Wu, Fangzhao Wu, Yang Cao, Yongfeng Huang, and Xing Xie · 2021

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Adversarial and contrastive variational autoencoder for sequential recommendation

Zhe Xie, Chengxuan Liu, Yichi Zhang, Hongtao Lu, Dong Wang, and Yue Ding · 2021

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A relevant and diverse retrieval-enhanced data augmentation framework for sequential recommendation

Shuqing Bian, Wayne Xin Zhao, Jinpeng Wang, and Ji-Rong Wen · 2022

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Intent contrastive learning for sequential recommendation

Yongjun Chen, Zhiwei Liu, Jia Li, Julian McAuley, and Caiming Xiong · 2022

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Contrastive learning for representation degeneration problem in sequential recommendation

Ruihong Qiu, Zi Huang, Hongzhi Yin, and Zijian Wang · 2022

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Temporal contrastive pre-training for sequential recommendation

Changxin Tian, Zihan Lin, Shuqing Bian, Jinpeng Wang, and Wayne Xin Zhao · 2022

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Learning to augment for casual user recommendation

Jianling Wang, Ya Le, Bo Chang, Yuyan Wang, Ed H Chi, and Minmin Chen · 2022

Earlier work this paper cites.

Explanation guided contrastive learning for sequential recommendation

Lei Wang, Ee-Peng Lim, Zhiwei Liu, and Tianxiang Zhao · 2022

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Contrastvae: Contrastive variational autoencoder for sequential recommendation

Yu Wang, Hengrui Zhang, Zhiwei Liu, Liangwei Yang, and Philip S Yu · 2022

Cited alongside, same era.

Contrastive learning for sequential recommendation

Xu Xie, Fei Sun, Zhaoyang Liu, Shiwen Wu, Jinyang Gao, Jiandong Zhang, Bolin Ding, and Bin Cui · 2022

Cited alongside, same era.

Image data augmentation for deep learning: A survey

Suorong Yang, Weikang Xiao, Mengcheng Zhang, Suhan Guo, Jian Zhao, and Furao Shen · 2022

Cited alongside, same era.

Ticoserec: Augmenting data to uniform sequences by time intervals for effective recommendation

Yizhou Dang, Enneng Yang, Guibing Guo, Linying Jiang, Xingwei Wang, Xiaoxiao Xu, Qinghui Sun, and Hong Liu · 2023

Cited alongside, same era.

Uniform sequence better: Time interval aware data augmentation for sequential recommendation

Yizhou Dang, Enneng Yang, Guibing Guo, Linying Jiang, Xingwei Wang, Xiaoxiao Xu, Qinghui Sun, and Hong Liu · 2023

Cited alongside, same era.

Equivariant contrastive learning for sequential recommendation

Peilin Zhou, Jingqi Gao, Yueqi Xie, Qichen Ye, Yining Hua, Jaeboum Kim, Shoujin Wang, and Sunghun Kim · 2023

Later among the works it cites.

Diffusion-based contrastive learning for sequential recommendation

Ziqiang Cui, Haolun Wu, Bowei He, Ji Cheng, and Chen Ma · 2024

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Repeated padding as data augmentation for sequential recommendation

Yizhou Dang, Yuting Liu, Enneng Yang, Guibing Guo, Linying Jiang, Xingwei Wang, and Jianzhe Zhao · 2024

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Data augmentation using llms: Data perspectives, learning paradigms and challenges

Bosheng Ding, Chengwei Qin, Ruochen Zhao, Tianze Luo, Xinze Li, Guizhen Chen, Wenhan Xia, Junjie Hu, Anh Tuan Luu, and Shafiq Joty · 2024

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A survey on data-centric recommender systems

Riwei Lai, Li Chen, Rui Chen, and Chi Zhang · 2024

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Integrating large language models into recommendation via mutual augmentation and adaptive aggregation