Fetching the paper…
Reading the bibliography…
Mainstream solutions to Sequential Recommendation (SR) represent items with fixed vectors.
An analysis for unreplicated fractional factorials
George EP Box and R Daniel Meyer. 1986 · 1986
Earlier work this paper cites.
An MDP-based recommender system
Guy Shani, David Heckerman, Ronen I Brafman, and Craig Boutilier. 2005 · 2005
Earlier work this paper cites.
Visualizing data using t-SNE
Laurens Van der Maaten and Geoffrey Hinton. 2008 · 2008
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.
Novelty and diversity in top-n recommendation–analysis and evaluation
Neil Hurley and Mi Zhang. 2011 · 2011
Earlier work this paper cites.
Auralist: introducing serendipity into music recommendation. In Proceedings of the fifth ACM international conference on Web search and data mining . 13–22
Yuan Cao Zhang, Diarmuid Ó Séaghdha, Daniele Quercia, and Tamas Jambor. 2012 · 2012
Earlier work this paper cites.
Using temporal data for making recommendations
Andrew Zimdars, David Maxwell Chickering, and Christopher Meek. 2013 · 2013
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy. 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.
U-net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18 . Springer, 234–241
Olaf Ronneberger, Philipp Fischer, and Thomas Brox. 2015 · 2015
Earlier work this paper cites.
Deep unsupervised learning using nonequilibrium thermodynamics. In International Conference on Machine Learning . PMLR, 2256–2265
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli. 2015 · 2015
Earlier work this paper cites.
Target Interest Distillation for Multi-Interest Recommendation. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management . 2007–2016
Chenyang Wang, Zhefan Wang, Yankai Liu, Yang Ge, Weizhi Ma, Min Zhang, Yiqun Liu, Junlan Feng, Chao Deng, and Shaoping Ma. 2022 · 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 · 2017
Earlier work this paper cites.
Sequential recommendation with user memory networks. In Proceedings of the eleventh ACM international conference on web search and data mining . 108–116
Xu Chen, Hongteng Xu, Yongfeng Zhang, Jiaxi Tang, Yixin Cao, Zheng Qin, and Hongyuan Zha. 2018 · 2018
Earlier work this paper cites.
Adversarial personalized ranking for recommendation. In The 41st International ACM SIGIR conference on research & development in information retrieval . 355–364
Xiangnan He, Zhankui He, Xiaoyu Du, and Tat-Seng Chua. 2018 · 2018
Earlier work this paper cites.
Recurrent neural networks with top-k gains for session-based recommendations. In Proceedings of the 27th ACM international conference on information and knowledge management . 843–852
Balázs Hidasi and Alexandros Karatzoglou. 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.
Variational autoencoders for collaborative filtering. In Proceedings of the 2018 world wide web conference . 689–698
Dawen Liang, Rahul G Krishnan, Matthew D Hoffman, and Tony Jebara. 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.
Deep interest network for click-through rate prediction. In Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining . 1059–1068
Guorui Zhou, Xiaoqiang Zhu, Chenru Song, Ying Fan, Han Zhu, Xiao Ma, Yanghui Yan, Junqi Jin, Han Li, and Kun Gai. 2018 · 2018
Earlier work this paper cites.
Multi-interest network with dynamic routing for recommendation at Tmall. In Proceedings of the 28th ACM international conference on information and knowledge management . 2615–2623
Chao Li, Zhiyuan Liu, Mengmeng Wu, Yuchi Xu, Huan Zhao, Pipei Huang, Guoliang Kang, Qiwei Chen, Wei Li, and Dik Lun Lee. 2019 · 2019
Cited alongside, same era.
Sequential variational autoencoders for collaborative filtering. In Proceedings of the twelfth ACM international conference on web search and data mining . 600–608
Noveen Sachdeva, Giuseppe Manco, Ettore Ritacco, and Vikram Pudi. 2019 · 2019
Cited alongside, same era.
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
Cited alongside, same era.
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
Blended diffusion for text-driven editing of natural images. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 18208–18218
Omri Avrahami, Dani Lischinski, and Ohad Fried. 2022 · 2022
Later among the works it cites.
Continuous diffusion for categorical data
Sander Dieleman, Laurent Sartran, Arman Roshannai, Nikolay Savinov, Yaroslav Ganin, Pierre H Richemond, Arnaud Doucet, Robin Strudel, Chris Dyer, Conor Durkan, et al · 2022
Later among the works it cites.
Diffuseq: Sequence to sequence text generation with diffusion models
Shansan Gong, Mukai Li, Jiangtao Feng, Zhiyong Wu, and LingPeng Kong. 2022 · 2022
Later among the works it cites.
Xiaochuang Han, Sachin Kumar, and Yulia Tsvetkov. 2022 · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Hierarchical neural variational model for personalized sequential recommendation. In The World Wide Web Conference . 3377–3383
Teng Xiao, Shangsong Liang, and Zaiqiao Meng. 2019 · 2019
Cited alongside, same era.
Infovae: Balancing learning and inference in variational autoencoders. In Proceedings of the aaai conference on artificial intelligence , Vol. 33. 5885–5892
Shengjia Zhao, Jiaming Song, and Stefano Ermon. 2019 · 2019
Cited alongside, same era.
Learning gradient fields for shape generation. In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part III 16 . Springer, 364–381
Ruojin Cai, Guandao Yang, Hadar Averbuch-Elor, Zekun Hao, Serge Belongie, Noah Snavely, and Bharath Hariharan. 2020 · 2020
Cited alongside, same era.
Controllable multi-interest framework for recommendation. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 2942–2951
Yukuo Cen, Jianwei Zhang, Xu Zou, Chang Zhou, Hongxia Yang, and Jie Tang. 2020 · 2020
Cited alongside, same era.
WaveGrad: Estimating gradients for waveform generation
Nanxin Chen, Yu Zhang, Heiga Zen, Ron J Weiss, Mohammad Norouzi, and William Chan. 2020 · 2020
Cited alongside, same era.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel. 2020 · 2020
Cited alongside, same era.
KERL: A knowledge-guided reinforcement learning model for sequential recommendation. In Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval . 209–218
Pengfei Wang, Yu Fan, Long Xia, Wayne Xin Zhao, ShaoZhang Niu, and Jimmy Huang. 2020 · 2020
Cited alongside, same era.
SSE-PT: Sequential recommendation via personalized transformer. In Proceedings of the 14th ACM Conference on Recommender Systems . 328–337
Liwei Wu, Shuqing Li, Cho-Jui Hsieh, and James Sharpnack. 2020 · 2020
Cited alongside, same era.
Zhengfu He, Tianxiang Sun, Kuanning Wang, Xuanjing Huang, and Xipeng Qiu. 2022 · 2022
Later among the works it cites.
Cascaded Diffusion Models for High Fidelity Image Generation
Jonathan Ho, Chitwan Saharia, William Chan, David J Fleet, Mohammad Norouzi, and Tim Salimans. 2022a · 2022
Later among the works it cites.
Jonathan Ho, Tim Salimans, Alexey Gritsenko, William Chan, Mohammad Norouzi, and David J Fleet. 2022b · 2022
Later among the works it cites.
On sampled metrics for item recommendation
Walid Krichene and Steffen Rendle. 2022 · 2022
Later among the works it cites.
Diffusion-LM Improves Controllable Text Generation
Xiang Lisa Li, John Thickstun, Ishaan Gulrajani, Percy Liang, and Tatsunori B Hashimoto. 2022 · 2022
Later among the works it cites.
Hierarchical text-conditional image generation with clip latents
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. 2022 · 2022
Later among the works it cites.
High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition . 10684–10695
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. 2022 · 2022
Later among the works it cites.
Image super-resolution via iterative refinement
Chitwan Saharia, Jonathan Ho, William Chan, Tim Salimans, David J Fleet, and Mohammad Norouzi. 2022 · 2022
Later among the works it cites.
Sequential Recommendation with User Evolving Preference Decomposition
Weiqi Shao, Xu Chen, Long Xia, Jiashu Zhao, and Dawei Yin. 2022 · 2022
Later among the works it cites.
Self-conditioned embedding diffusion for text generation
Robin Strudel, Corentin Tallec, Florent Altché, Yilun Du, Yaroslav Ganin, Arthur Mensch, Will Grathwohl, Nikolay Savinov, Sander Dieleman, Laurent Sifre, et al · 2022
Later among the works it cites.
When Multi-Level Meets Multi-Interest: A Multi-Grained Neural Model for Sequential Recommendation. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval . 1632–1641
Yu Tian, Jianxin Chang, Yanan Niu, Yang Song, and Chenliang Li. 2022 · 2022
Later among the works it cites.
Diffusion models: A comprehensive survey of methods and applications
Ling Yang, Zhilong Zhang, Yang Song, Shenda Hong, Runsheng Xu, Yue Zhao, Yingxia Shao, Wentao Zhang, Bin Cui, and Ming-Hsuan Yang. 2022 · 2022
Later among the works it cites.
Re4: Learning to Re-contrast, Re-attend, Re-construct for Multi-interest Recommendation. In Proceedings of the ACM Web Conference 2022 . 2216–2226
Shengyu Zhang, Lingxiao Yang, Dong Yao, Yujie Lu, Fuli Feng, Zhou Zhao, Tat-Seng Chua, and Fei Wu. 2022 · 2022
Later among the works it cites.
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
Later among the works it cites.
TESS: Text-to-Text Self-Conditioned Simplex Diffusion
Rabeeh Karimi Mahabadi, Jaesung Tae, Hamish Ivison, James Henderson, Iz Beltagy, Matthew E Peters, and Arman Cohan. 2023 · 2023
Closest in time.
Sequential recommendation via stochastic self-attention. In Proceedings of the ACM Web Conference 2022 . 2036–2047
Ziwei Fan, Zhiwei Liu, Yu Wang, Alice Wang, Zahra Nazari, Lei Zheng, Hao Peng, and Philip S Yu. 2022 · 2047
Closest in time.