Fetching the paper…
Reading the bibliography…
With the thriving of pre-trained language model (PLM) widely verified in various of NLP tasks, pioneer efforts attempt to explore the possible cooperation of the general textual information in PLM with the personalized behavioral information in user historical behavior sequences to enhance sequential recommendation (SR).
“Language models are few-shot learners”
Tom Brown et al · 1901
Earlier work this paper cites.
“S3-rec: Self-supervised learning for sequential recommendation with mutual information maximization”
Kun Zhou et al · 1902
Earlier work this paper cites.
“Recurrent neural network based language model.”
Tomas Mikolov et al · 2010
Earlier work this paper cites.
“Factorizing personalized markov chains for next-basket recommendation”
Steffen Rendle, Christoph Freudenthaler and Lars Schmidt-Thieme · 2010
Earlier work this paper cites.
“Learning phrase representations using RNN encoder-decoder for statistical machine translation”
Kyunghyun Cho et al · 2014
Earlier work this paper cites.
“Session-based recommendations with recurrent neural networks”
Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas and Domonkos Tikk · 2015
Earlier work this paper cites.
“Neural machine translation of rare words with subword units”
Rico Sennrich, Barry Haddow and Alexandra Birch · 2015
Earlier work this paper cites.
“Hashtag recommendation using attention-based convolutional neural network.”
Yuyun Gong and Qi Zhang · 2016
Earlier work this paper cites.
“Fusing similarity models with markov chains for sparse sequential recommendation”
Ruining He and Julian McAuley · 2016
Earlier work this paper cites.
“Collaborative knowledge base embedding for recommender systems”
Fuzheng Zhang et al · 2016
Earlier work this paper cites.
“Sequential user-based recurrent neural network recommendations”
Tim Donkers, Benedikt Loepp and Jürgen Ziegler · 2017
Earlier work this paper cites.
“Translation-based recommendation”
Ruining He, Wang-Cheng Kang and Julian McAuley · 2017
Earlier work this paper cites.
“Attention is all you need”
Ashish Vaswani et al · 2017
Earlier work this paper cites.
“Joint deep modeling of users and items using reviews for recommendation”
Lei Zheng, Vahid Noroozi and Philip Yu · 2017
Earlier work this paper cites.
“Bert: Pre-training of deep bidirectional transformers for language understanding”
Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova · 2018
Earlier work this paper cites.
“Improving sequential recommendation with knowledge-enhanced memory networks”
Jin Huang et al · 2018
Earlier work this paper cites.
“Self-attentive sequential recommendation”
Wang-Cheng Kang and Julian McAuley · 2018
Earlier work this paper cites.
“Personalized top-n sequential recommendation via convolutional sequence embedding”
Jiaxi Tang and Ke Wang · 2018
Earlier work this paper cites.
“ π \pi -net: A parallel information-sharing network for shared-account cross-domain sequential recommendations”
Muyang Ma et al · 2019
Earlier work this paper cites.
“Justifying recommendations using distantly-labeled reviews and fine-grained aspects”
Jianmo Ni, Jiacheng Li and Julian McAuley · 2019
Earlier work this paper cites.
“BERT4Rec: Sequential recommendation with bidirectional encoder representations from transformer”
Fei Sun et al · 2019
Cited alongside, same era.
“Session-based recommendation with graph neural networks”
Shu Wu et al · 2019
Cited alongside, same era.
“Longformer: The long-document transformer”
Iz Beltagy, Matthew Peters and Arman Cohan · 2020
Cited alongside, same era.
“An image is worth 16x16 words: Transformers for image recognition at scale”
Alexey Dosovitskiy et al · 2020
Cited alongside, same era.
“Exploring the limits of transfer learning with a unified text-to-text transformer”
Colin Raffel et al · 2020
Cited alongside, same era.
“Towards Universal Sequence Representation Learning for Recommender Systems”
Yupeng Hou et al · 2022
Later among the works it cites.
“Contrastive learning for representation degeneration problem in sequential recommendation”
Ruihong Qiu, Zi Huang, Hongzhi Yin and Zijian Wang · 2022
Later among the works it cites.
“TransRec: Learning Transferable Recommendation from Mixture-of-Modality Feedback”
Jie Wang et al · 2022
Later among the works it cites.
“Personalized prompts for sequential recommendation”
Yiqing Wu et al · 2022
Later among the works it cites.
“Selective fairness in recommendation via prompts”
Yiqing Wu et al · 2022
Later among the works it cites.
“Contrastive learning for sequential recommendation”
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Thomas Wolf et al · 2020
Cited alongside, same era.
“Parameter-efficient transfer from sequential behaviors for user modeling and recommendation”
Fajie Yuan, Xiangnan He, Alexandros Karatzoglou and Liguang Zhang · 2020
Cited alongside, same era.
“Adversarial feature translation for multi-domain recommendation”
Xiaobo Hao et al · 2021
Cited alongside, same era.
“Swin transformer: Hierarchical vision transformer using shifted windows”
Ze Liu et al · 2021
Cited alongside, same era.
“Empowering news recommendation with pre-trained language models”
Chuhan Wu, Fangzhao Wu, Tao Qi and Yongfeng Huang · 2021
Cited alongside, same era.
“Uprec: User-aware pre-training for recommender systems”
Chaojun Xiao et al · 2021
Cited alongside, same era.
“Long-and short-term self-attention network for sequential recommendation”
Chengfeng Xu et al · 2021
Cited alongside, same era.
Xu Xie et al · 2022
Later among the works it cites.
Keqin Bao et al · 2023
Closest in time.
“Longnet: Scaling transformers to 1,000,000,000 tokens”
Jiayu Ding et al · 2023
Closest in time.
“Chat-rec: Towards interactive and explainable llms-augmented recommender system”
Yunfan Gao et al · 2023
Closest in time.
“VIP5: Towards Multimodal Foundation Models for Recommendation”
Shijie Geng et al · 2023
Closest in time.
“Large language models are zero-shot rankers for recommender systems”
Yupeng Hou et al · 2023
Closest in time.
“Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Prediction”
Wang-Cheng Kang et al · 2023
Closest in time.
“Text Is All You Need: Learning Language Representations for Sequential Recommendation”
Jiacheng Li et al · 2023
Closest in time.
“Is ChatGPT a Good Recommender? A Preliminary Study”
Junling Liu et al · 2023
Closest in time.
“ID-Agnostic User Behavior Pre-training for Sequential Recommendation”
Shanlei Mu et al · 2023
Closest in time.
“Zero-Shot Next-Item Recommendation using Large Pretrained Language Models”
Lei Wang and Ee-Peng Lim · 2023
Closest in time.
“Attacking Pre-trained Recommendation”
Yiqing Wu et al · 2023
Closest in time.
“UPRec: User-aware Pre-training for sequential Recommendation”
Chaojun Xiao et al · 2023
Closest in time.
“AgentCF: Collaborative Learning with Autonomous Language Agents for Recommender Systems”
Junjie Zhang et al · 2023
Closest in time.
“Recommendation as instruction following: A large language model empowered recommendation approach”
Junjie Zhang et al · 2023
Closest in time.