Fetching the paper…
Reading the bibliography…
Recent advances in Large Language Models (LLMs) have demonstrated promising performance in sequential recommendation tasks, leveraging their superior language understanding capabilities.
Collaborative filtering recommender systems
J Ben Schafer, Dan Frankowski, Jon Herlocker, and Shilad Sen. 2007 · 2007
Earlier work this paper cites.
Factorizing personalized markov chains for next-basket recommendation. In Proceedings of the 19th international conference on World wide web . 811–820
Steffen Rendle, Christoph Freudenthaler, and Lars Schmidt-Thieme. 2010 · 2010
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.
Learning hierarchical representation model for nextbasket recommendation. In Proceedings of the 38th International ACM SIGIR conference on Research and Development in Information Retrieval . 403–412
Pengfei Wang, Jiafeng Guo, Yanyan Lan, Jun Xu, Shengxian Wan, and Xueqi Cheng. 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. 2016a · 2016
Earlier work this paper cites.
Neural attentive session-based recommendation. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management . 1419–1428
Jing Li, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Tao Lian, and Jun Ma. 2017 · 2017
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin. 2018 · 2018
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.
Personalized top-n sequential recommendation via convolutional sequence embedding. In Proceedings of the eleventh ACM international conference on web search and data mining . 565–573
Jiaxi Tang and Ke Wang. 2018 · 2018
Earlier work this paper cites.
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics . Association for Computational Linguistics, 4171–4186
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 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.
Session-based recommendation with graph neural networks. In Proceedings of the AAAI conference on artificial intelligence , Vol. 33. 346–353
Shu Wu, Yuyuan Tang, Yanqiao Zhu, Liang Wang, Xing Xie, and Tieniu Tan. 2019 · 2019
Earlier work this paper cites.
A simple convolutional generative network for next item recommendation. In Proceedings of the twelfth ACM international conference on web search and data mining . 582–590
Fajie Yuan, Alexandros Karatzoglou, Ioannis Arapakis, Joemon M Jose, and Xiangnan He. 2019 · 2019
Earlier work this paper cites.
Language models are few-shot learners
Tom B Brown. 2020 · 2020
Earlier work this paper cites.
Sequential recommendation with graph neural networks. In Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval . 378–387
Jianxin Chang, Chen Gao, Yu Zheng, Yiqun Hui, Yanan Niu, Yang Song, Depeng Jin, and Yong Li. 2021 · 2021
Cited alongside, same era.
Soundstream: An end-to-end neural audio codec
Neil Zeghidour, Alejandro Luebs, Ahmed Omran, Jan Skoglund, and Marco Tagliasacchi. 2021 · 2021
Cited alongside, same era.
M6-rec: Generative pretrained language models are open-ended recommender systems
Zeyu Cui, Jianxin Ma, Chang Zhou, Jingren Zhou, and Hongxia Yang. 2022 · 2022
Cited alongside, same era.
Recommendation as language processing (rlp): A unified pretrain, personalized prompt & predict paradigm (p5). In Proceedings of the 16th ACM Conference on Recommender Systems . 299–315
Shijie Geng, Shuchang Liu, Zuohui Fu, Yingqiang Ge, and Yongfeng Zhang. 2022 · 2022
Cited alongside, same era.
LlamaRec: Two-stage recommendation using large language models for ranking
Zhenrui Yue, Sara Rabhi, Gabriel de Souza Pereira Moreira, Dong Wang, and Even Oldridge. 2023 · 2023
Later among the works it cites.
Collm: Integrating collaborative embeddings into large language models for recommendation
Yang Zhang, Fuli Feng, Jizhi Zhang, Keqin Bao, Qifan Wang, and Xiangnan He. 2023 · 2023
Later among the works it cites.
Qlora: Efficient finetuning of quantized llms
Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, and Luke Zettlemoyer. 2024 · 2024
Closest in time.
Bridging Items and Language: A Transition Paradigm for Large Language Model-Based Recommendation. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining . 1816–1826
Xinyu Lin, Wenjie Wang, Yongqi Li, Fuli Feng, See-Kiong Ng, and Tat-Seng Chua. 2024 · 2024
Closest in time.
A Practice-Friendly Two-Stage LLM-Enhanced Paradigm in Sequential Recommendation
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Autoregressive image generation using residual quantization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 11523–11532
Doyup Lee, Chiheon Kim, Saehoon Kim, Minsu Cho, and Wook-Shin Han. 2022 · 2022
Cited alongside, same era.
Expertbert: Pretraining expert finding. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management . 4244–4248
Hongtao Liu, Zhepeng Lv, Qing Yang, Dongliang Xu, and Qiyao Peng. 2022 · 2022
Cited alongside, same era.
Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al · 2022
Cited alongside, same era.
Expertplm: pre-training expert representation for expert finding. In Findings of the Association for Computational Linguistics: EMNLP 2022 . 1043–1052
Qiyao Peng and Hongtao Liu. 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.
Tallrec: An effective and efficient tuning framework to align large language model with recommendation. In Proceedings of the 17th ACM Conference on Recommender Systems . 1007–1014
Keqin Bao, Jizhi Zhang, Yang Zhang, Wenjie Wang, Fuli Feng, and Xiangnan He. 2023 · 2023
Cited alongside, same era.
Prompt distillation for efficient llm-based recommendation. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management . 1348–1357
Lei Li, Yongfeng Zhang, and Li Chen. 2023 · 2023
Cited alongside, same era.
Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al · 2023
Cited alongside, same era.
Dugang Liu, Shenxian Xian, Xiaolin Lin, Xiaolian Zhang, Hong Zhu, Yuan Fang, Zhen Chen, and Zhong Ming. 2024b · 2024
Closest in time.
Bucket Pre-training is All You Need
Hongtao Liu, Qiyao Peng, Qing Yang, Kai Liu, and Hongyan Xu. 2024a · 2024
Closest in time.
PEPT: Expert Finding Meets Personalized Pre-training
Qiyao Peng, Hongyan Xu, Yinghui Wang, Hongtao Liu, Cuiying Huo, and Wenjun Wang. 2024 · 2024
Closest in time.
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 · 2024
Closest in time.
Preference Distillation for Personalized Generative Recommendation
Jerome Ramos, Bin Wu, and Aldo Lipani. 2024 · 2024
Closest in time.
Idgenrec: Llm-recsys alignment with textual id learning. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval . 355–364
Juntao Tan, Shuyuan Xu, Wenyue Hua, Yingqiang Ge, Zelong Li, and Yongfeng Zhang. 2024 · 2024
Closest in time.
RDRec: Rationale Distillation for LLM-based Recommendation
Xinfeng Wang, Jin Cui, Yoshimi Suzuki, and Fumiyo Fukumoto. 2024 · 2024
Closest in time.
Linear recurrent units for sequential recommendation. In Proceedings of the 17th ACM International Conference on Web Search and Data Mining . 930–938
Zhenrui Yue, Yueqi Wang, Zhankui He, Huimin Zeng, Julian McAuley, and Dong Wang. 2024 · 2024
Closest in time.
Adapting large language models by integrating collaborative semantics for recommendation. In 2024 IEEE 40th International Conference on Data Engineering (ICDE) . IEEE, 1435–1448
Bowen Zheng, Yupeng Hou, Hongyu Lu, Yu Chen, Wayne Xin Zhao, Ming Chen, and Ji-Rong Wen. 2024 · 2024
Closest in time.