Fetching the paper…
Reading the bibliography…
Large language models (LLMs) have demonstrated exceptional performance in understanding and generating semantic patterns, making them promising candidates for sequential recommendation tasks.
S3-rec: Self-supervised learning for sequential recommendation with mutual information maximization. In Proceedings of the 29th ACM international conference on information & knowledge management . 1893–1902
Kun Zhou, Hui Wang, Wayne Xin Zhao, Yutao Zhu, Sirui Wang, Fuzheng Zhang, Zhongyuan Wang, and Ji-Rong Wen. 2020 · 1902
Earlier work this paper cites.
An MDP-based recommender system
Guy Shani, David Heckerman, and Ronen I Brafman. 2005 · 2005
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma. 2014 · 2014
Earlier work this paper cites.
Fitnets: Hints for thin deep nets
Adriana Romero, Nicolas Ballas, Samira Ebrahimi Kahou, Antoine Chassang, Carlo Gatta, and Yoshua Bengio. 2014 · 2014
Earlier work this paper cites.
Session-based recommendations with recurrent neural networks
Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2015 · 2015
Earlier work this paper cites.
Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. 2015 · 2015
Earlier work this paper cites.
Image-based recommendations on styles and substitutes. In Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval . 43–52
Julian McAuley, Christopher Targett, Qinfeng Shi, and Anton Van Den Hengel. 2015 · 2015
Earlier work this paper cites.
Fusing similarity models with markov chains for sparse sequential recommendation. In 2016 IEEE 16th international conference on data mining (ICDM) . IEEE, 191–200
Ruining He and Julian McAuley. 2016 · 2016
Earlier work this paper cites.
Contextual sequence modeling for recommendation with recurrent neural networks. In Proceedings of the 2nd workshop on deep learning for recommender systems . 2–9
Elena Smirnova and Flavian Vasile. 2017 · 2017
Earlier work this paper cites.
Zheng Xu, Yen-Chang Hsu, and Jiawei Huang. 2017 · 2017
Earlier work this paper cites.
A gift from knowledge distillation: Fast optimization, network minimization and transfer learning. In Proceedings of the IEEE conference on computer vision and pattern recognition . 4133–4141
Junho Yim, Donggyu Joo, Jihoon Bae, and Junmo Kim. 2017 · 2017
Earlier work this paper cites.
Self-attentive sequential recommendation. In 2018 IEEE international conference on data mining (ICDM) . IEEE, 197–206
Wang-Cheng Kang and Julian McAuley. 2018 · 2018
Earlier work this paper cites.
Knowledge transfer via distillation of activation boundaries formed by hidden neurons. In Proceedings of the AAAI conference on artificial intelligence , Vol. 33. 3779–3787
Byeongho Heo, Minsik Lee, Sangdoo Yun, and Jin Young Choi. 2019 · 2019
Earlier work this paper cites.
BERT4Rec: Sequential recommendation with bidirectional encoder representations from transformer. In Proceedings of the 28th ACM international conference on information and knowledge management . 1441–1450
Fei Sun, Jun Liu, Jian Wu, Changhua Pei, Xiao Lin, Wenwu Ou, and Peng Jiang. 2019 · 2019
Earlier work this paper cites.
Recurrent convolutional neural network for sequential recommendation. In The world wide web conference . 3398–3404
Chengfeng Xu, Pengpeng Zhao, Yanchi Liu, Jiajie Xu, Victor S Sheng S. Sheng, Zhiming Cui, Xiaofang Zhou, and Hui Xiong. 2019 · 2019
Earlier work this paper cites.
Time interval aware self-attention for sequential recommendation. In Proceedings of the 13th international conference on web search and data mining . 322–330
Jiacheng Li, Yujie Wang, and Julian McAuley. 2020 · 2020
Earlier work this paper cites.
Transformers4rec: Bridging the gap between nlp and sequential/session-based recommendation. In Proceedings of the 15th ACM conference on recommender systems . 143–153
Gabriel de Souza Pereira Moreira, Sara Rabhi, Jeong Min Lee, Ronay Ak, and Even Oldridge. 2021 · 2021
Earlier work this paper cites.
Knowledge distillation: A survey
Jianping Gou, Baosheng Yu, Stephen J Maybank, and Dacheng Tao. 2021 · 2021
Cited alongside, same era.
Long-and short-term self-attention network for sequential recommendation
Chengfeng Xu, Jian Feng, Pengpeng Zhao, Fuzhen Zhuang, Deqing Wang, Yanchi Liu, and Victor S Sheng. 2021 · 2021
Cited alongside, same era.
Mutual-learning improves end-to-end speech translation. In Proceedings of the 2021 conference on empirical methods in natural language processing . 3989–3994
Jiawei Zhao, Wei Luo, Boxing Chen, and Andrew Gilman. 2021 · 2021
Cited alongside, same era.
Mutual distillation learning network for trajectory-user linking
Wei Chen, Shuzhe Li, Chao Huang, Yanwei Yu, Yongguo Jiang, and Junyu Dong. 2022 · 2022
Cited alongside, same era.
Lora: Low-rank adaptation of large language models
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen, et al · 2022
Cited alongside, same era.
Large language model distilling medication recommendation model
Qidong Liu, Xian Wu, Xiangyu Zhao, Yuanshao Zhu, Zijian Zhang, Feng Tian, and Yefeng Zheng. 2024b · 2024
Later among the works it cites.
Aligning Large Language Models for Controllable Recommendations. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . 8159–8172
Wensheng Lu, Jianxun Lian, Wei Zhang, Guanghua Li, Mingyang Zhou, Hao Liao, and Xing Xie. 2024 · 2024
Later among the works it cites.
Towards open-world recommendation with knowledge augmentation from large language models. In Proceedings of the 18th ACM Conference on Recommender Systems . 12–22
Yunjia Xi, Weiwen Liu, Jianghao Lin, Xiaoling Cai, Hong Zhu, Jieming Zhu, Bo Chen, Ruiming Tang, Weinan Zhang, and Yong Yu. 2024 · 2024
Later among the works it cites.
Slmrec: empowering small language models for sequential recommendation
Wujiang Xu, Zujie Liang, Jiaojiao Han, Xuying Ning, Wenfang Lin, Linxun Chen, Feng Wei, and Yongfeng Zhang. 2024 · 2024
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Parameter-free dynamic graph embedding for link prediction
Jiahao Liu, Dongsheng Li, Hansu Gu, Tun Lu, Peng Zhang, and Ning Gu. 2022 · 2022
Cited alongside, same era.
Filter-enhanced MLP is all you need for sequential recommendation. In Proceedings of the ACM web conference 2022 . 2388–2399
Kun Zhou, Hui Yu, Wayne Xin Zhao, and Ji-Rong Wen. 2022 · 2022
Cited alongside, same era.
Xuxin Cheng, Bowen Cao, Qichen Ye, Zhihong Zhu, Hongxiang Li, and Yuexian Zou. 2023 · 2023
Cited alongside, same era.
Leveraging large language models for sequential recommendation. In Proceedings of the 17th ACM Conference on Recommender Systems . 1096–1102
Jesse Harte, Wouter Zorgdrager, Panos Louridas, Asterios Katsifodimos, Dietmar Jannach, and Marios Fragkoulis. 2023 · 2023
Cited alongside, same era.
Xinhang Li, Chong Chen, Xiangyu Zhao, Yong Zhang, and Chunxiao Xing. 2023a · 2023
Cited alongside, same era.
Recommendation unlearning via matrix correction
Jiahao Liu, Dongsheng Li, Hansu Gu, Tun Lu, Jiongran Wu, Peng Zhang, Li Shang, and Ning Gu. 2023a · 2023
Cited alongside, same era.
Personalized graph signal processing for collaborative filtering. In Proceedings of the ACM Web Conference 2023 . 1264–1272
Jiahao Liu, Dongsheng Li, Hansu Gu, Tun Lu, Peng Zhang, Li Shang, and Ning Gu. 2023b · 2023
Cited alongside, same era.
DELRec: Distilling Sequential Pattern to Enhance LLMs-based Sequential Recommendation
Haoyi Zhang, Guohao Sun, Jinhu Lu, Guanfeng Liu, and Xiu Susie Fang. 2024b · 2024
Later among the works it cites.
Agentcf: Collaborative learning with autonomous language agents for recommender systems. In Proceedings of the ACM on Web Conference 2024 . 3679–3689
Junjie Zhang, Yupeng Hou, Ruobing Xie, Wenqi Sun, Julian McAuley, Wayne Xin Zhao, Leyu Lin, and Ji-Rong Wen. 2024a · 2024
Later among the works it cites.
Harnessing large language models for text-rich sequential recommendation. In Proceedings of the ACM Web Conference 2024 . 3207–3216
Zhi Zheng, Wenshuo Chao, Zhaopeng Qiu, Hengshu Zhu, and Hui Xiong. 2024 · 2024
Later among the works it cites.
Active Large Language Model-based Knowledge Distillation for Session-based Recommendation. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 39. 11607–11615
Yingpeng Du, Zhu Sun, Ziyan Wang, Haoyan Chua, Jie Zhang, and Yew-Soon Ong. 2025 · 2025
Closest in time.
LLM-Based User Simulation for Low-Knowledge Shilling Attacks on Recommender Systems
Shengkang Gu, Jiahao Liu, Dongsheng Li, Guangping Zhang, Mingzhe Han, Hansu Gu, Peng Zhang, Ning Gu, Li Shang, and Tun Lu. 2025 · 2025
Closest in time.
FedCIA: Federated Collaborative Information Aggregation for Privacy-Preserving Recommendation
Mingzhe Han, Dongsheng Li, Jiafeng Xia, Jiahao Liu, Hansu Gu, Peng Zhang, Ning Gu, and Tun Lu. 2025 · 2025
Closest in time.
Lost in Sequence: Do Large Language Models Understand Sequential Recommendation?
Sein Kim, Hongseok Kang, Kibum Kim, Jiwan Kim, Donghyun Kim, Minchul Yang, Kwangjin Oh, Julian McAuley, and Chanyoung Park. 2025 · 2025
Closest in time.
Enhancing Cross-Domain Recommendations with Memory-Optimized LLM-Based User Agents
Jiahao Liu, Shengkang Gu, Dongsheng Li, Guangping Zhang, Mingzhe Han, Hansu Gu, Peng Zhang, Tun Lu, Li Shang, and Ning Gu. 2025a · 2025
Closest in time.
Mitigating Popularity Bias in Collaborative Filtering through Fair Sampling
Jiahao Liu, Dongsheng Li, Hansu Gu, Peng Zhang, Tun Lu, Li Shang, and Ning Gu. 2025b · 2025
Closest in time.
Filtering Discomforting Recommendations with Large Language Models. In Proceedings of the ACM on Web Conference 2025 . 3639–3650
Jiahao Liu, Yiyang Shao, Peng Zhang, Dongsheng Li, Hansu Gu, Chao Chen, Longzhi Du, Tun Lu, and Ning Gu. 2025c · 2025
Closest in time.
Enhancing LLM-Based Recommendations Through Personalized Reasoning
Jiahao Liu, Xueshuo Yan, Dongsheng Li, Guangping Zhang, Hansu Gu, Peng Zhang, Tun Lu, Li Shang, and Ning Gu. 2025d · 2025
Closest in time.
Preference-Consistent Knowledge Distillation for Recommender System
Zhangchi Zhu and Wei Zhang. 2025 · 2025
Closest in time.