Fetching the paper…
Reading the bibliography…
Sequential Recommendation (SR) task involves predicting the next item a user is likely to interact with, given their past interactions.
Second order derivatives for network pruning: Optimal brain surgeon
Babak Hassibi and David Stork · 1992
Earlier work this paper cites.
Item-based collaborative filtering recommendation algorithms
Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl · 2001
Earlier work this paper cites.
Cumulated gain-based evaluation of ir techniques
Kalervo Järvelin and Jaana Kekäläinen · 2002
Earlier work this paper cites.
The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains
David I Shuman, Sunil K Narang, Pascal Frossard, Antonio Ortega, and Pierre Vandergheynst · 2013
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.
Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
Earlier work this paper cites.
How to learn a graph from smooth signals
Vassilis Kalofolias · 2016
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
Recurrent recommender networks
Chao-Yuan Wu, Amr Ahmed, Alex Beutel, Alexander J Smola, and How Jing · 2017
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.
Reducing transformer depth on demand with structured dropout
Angela Fan, Edouard Grave, and Armand Joulin · 2019
Earlier work this paper cites.
Parameter-efficient transfer learning for nlp
Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly · 2019
Earlier work this paper cites.
Hierarchical gating networks for sequential recommendation
Chen Ma, Peng Kang, and Xue Liu · 2019
Earlier work this paper cites.
Are sixteen heads really better than one?
Paul Michel, Omer Levy, and Graham Neubig · 2019
Earlier work this paper cites.
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
Earlier work this paper cites.
Analyzing multi-head self-attention: Specialized heads do the heavy lifting, the rest can be pruned
Elena Voita, David Talbot, Fedor Moiseev, Rico Sennrich, and Ivan Titov · 2019
Earlier work this paper cites.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
Earlier work this paper cites.
Lightgcn: Simplifying and powering graph convolution network for recommendation
Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, and Meng Wang · 2020
Earlier work this paper cites.
Dynabert: Dynamic bert with adaptive width and depth
Lu Hou, Zhiqi Huang, Lifeng Shang, Xin Jiang, Xiao Chen, and Qun Liu · 2020
Earlier work this paper cites.
Tinybert: Distilling bert for natural language understanding
Xiaoqi Jiao, Yichun Yin, Lifeng Shang, Xin Jiang, Xiao Chen, Linlin Li, Fang Wang, and Qun Liu · 2020
Earlier work this paper cites.
Scaling laws for neural language models
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei · 2020
Earlier work this paper cites.
Fastformers: Highly efficient transformer models for natural language understanding
Young Jin Kim and Hany Hassan Awadalla · 2020
Earlier work this paper cites.
On sampled metrics for item recommendation
Walid Krichene and Steffen Rendle · 2020
Cited alongside, same era.
Accelerating training of transformer-based language models with progressive layer dropping
Minjia Zhang and Yuxiong He · 2020
Cited alongside, same era.
Revisiting alternative experimental settings for evaluating top-n item recommendation algorithms
Wayne Xin Zhao, Junhua Chen, Pengfei Wang, Qi Gu, and Ji-Rong Wen · 2020
Cited alongside, same era.
S3-rec: Self-supervised learning for sequential recommendation with mutual information maximization
Kun Zhou, Hui Wang, Wayne Xin Zhao, Yutao Zhu, Sirui Wang, Fuzheng Zhang, Zhongyuan Wang, and Ji-Rong Wen · 2020
Cited alongside, same era.
Lighter and better: low-rank decomposed self-attention networks for next-item recommendation
Xinyan Fan, Zheng Liu, Jianxun Lian, Wayne Xin Zhao, Xing Xie, and Ji-Rong Wen · 2021
Cited alongside, same era.
Llara: Aligning large language models with sequential recommenders
Jiayi Liao, Sihang Li, Zhengyi Yang, Jiancan Wu, Yancheng Yuan, Xiang Wang, and Xiangnan He · 2023
Later among the works it cites.
Lightlm: a lightweight deep and narrow language model for generative recommendation
Kai Mei and Yongfeng Zhang · 2023
Later among the works it cites.
gsasrec: Reducing overconfidence in sequential recommendation trained with negative sampling
Aleksandr Vladimirovich Petrov and Craig Macdonald · 2023
Later among the works it cites.
On the effect of dropping layers of pre-trained transformer models
Hassan Sajjad, Fahim Dalvi, Nadir Durrani, and Preslav Nakov · 2023
Later among the works it cites.
Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al · 2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Brian Lester, Rami Al-Rfou, and Noah Constant · 2021
Cited alongside, same era.
Recbole: Towards a unified, comprehensive and efficient framework for recommendation algorithms
Wayne Xin Zhao, Shanlei Mu, Yupeng Hou, Zihan Lin, Yushuo Chen, Xingyu Pan, Kaiyuan Li, Yujie Lu, Hui Wang, Changxin Tian, et al · 2021
Cited alongside, same era.
Understanding scaling laws for recommendation models
Newsha Ardalani, Carole-Jean Wu, Zeliang Chen, Bhargav Bhushanam, and Adnan Aziz · 2022
Cited alongside, same era.
Knowledge neurons in pretrained transformers
Damai Dai, Li Dong, Yaru Hao, Zhifang Sui, Baobao Chang, and Furu Wei · 2022
Cited alongside, same era.
p p -Laplacian based graph neural networks
Guoji Fu, Peilin Zhao, and Yatao Bian · 2022
Cited alongside, same era.
Recommendation as language processing (rlp): A unified pretrain, personalized prompt & predict paradigm (p5)
Shijie Geng, Shuchang Liu, Zuohui Fu, Yingqiang Ge, and Yongfeng Zhang · 2022
Cited alongside, same era.
Training compute-optimal large language models
Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, et al · 2022
Cited alongside, same era.
Later among the works it cites.
Neural node matching for multi-target cross domain recommendation
Wujiang Xu, Shaoshuai Li, Mingming Ha, Xiaobo Guo, Qiongxu Ma, Xiaolei Liu, Linxun Chen, and Zhenfeng Zhu · 2023
Later among the works it cites.
Graph masked autoencoder for sequential recommendation
Yaowen Ye, Lianghao Xia, and Chao Huang · 2023
Later among the works it cites.
Collaborative large language model for recommender systems
Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, and Jundong Li · 2023
Later among the works it cites.
Slicegpt: Compress large language models by deleting rows and columns
Saleh Ashkboos, Maximilian L Croci, Marcelo Gennari do Nascimento, Torsten Hoefler, and James Hensman · 2024
Closest in time.
The unreasonable ineffectiveness of the deeper layers
Andrey Gromov, Kushal Tirumala, Hassan Shapourian, Paolo Glorioso, and Daniel A Roberts · 2024
Closest in time.
MiniLLM: Knowledge distillation of large language models
Yuxian Gu, Li Dong, Furu Wei, and Minlie Huang · 2024
Closest in time.
Does localization inform editing? surprising differences in causality-based localization vs. knowledge editing in language models
Peter Hase, Mohit Bansal, Been Kim, and Asma Ghandeharioun · 2024
Closest in time.
Genrec: Large language model for generative recommendation
Jianchao Ji, Zelong Li, Shuyuan Xu, Wenyue Hua, Yingqiang Ge, Juntao Tan, and Yongfeng Zhang · 2024
Closest in time.
Shortgpt: Layers in large language models are more redundant than you expect
Xin Men, Mingyu Xu, Qingyu Zhang, Bingning Wang, Hongyu Lin, Yaojie Lu, Xianpei Han, and Weipeng Chen · 2024
Closest in time.
Roformer: Enhanced transformer with rotary position embedding
Jianlin Su, Murtadha Ahmed, Yu Lu, Shengfeng Pan, Wen Bo, and Yunfeng Liu · 2024
Closest in time.
Rethinking large language model architectures for sequential recommendations
Hanbing Wang, Xiaorui Liu, Wenqi Fan, Xiangyu Zhao, Venkataramana Kini, Devendra Yadav, Fei Wang, Zhen Wen, Jiliang Tang, and Hui Liu · 2024
Closest in time.
Llmrec: Large language models with graph augmentation for recommendation
Wei Wei, Xubin Ren, Jiabin Tang, Qinyong Wang, Lixin Su, Suqi Cheng, Junfeng Wang, Dawei Yin, and Chao Huang · 2024
Closest in time.
Simplifying and empowering transformers for large-graph representations
Qitian Wu, Wentao Zhao, Chenxiao Yang, Hengrui Zhang, Fan Nie, Haitian Jiang, Yatao Bian, and Junchi Yan · 2024
Closest in time.
Rethinking cross-domain sequential recommendation under open-world assumptions
Wujiang Xu, Qitian Wu, Runzhong Wang, Mingming Ha, Qiongxu Ma, Linxun Chen, Bing Han, and Junchi Yan · 2024
Closest in time.
Jiaqi Zhai, Lucy Liao, Xing Liu, Yueming Wang, Rui Li, Xuan Cao, Leon Gao, Zhaojie Gong, Fangda Gu, Michael He, et al · 2024
Closest in time.
Wukong: Towards a scaling law for large-scale recommendation
Buyun Zhang, Liang Luo, Yuxin Chen, Jade Nie, Xi Liu, Daifeng Guo, Yanli Zhao, Shen Li, Yuchen Hao, Yantao Yao, et al · 2024
Closest in time.
Exploring concept depth: How large language models acquire knowledge and concept at different layers?
Mingyu Jin, Qinkai Yu, Jingyuan Huang, Qingcheng Zeng, Zhenting Wang, Wenyue Hua, Haiyan Zhao, Kai Mei, Yanda Meng, Kaize Ding, Fan Yang, Mengnan Du, and Yongfeng Zhang · 2025
Closest in time.