Fetching the paper…
Reading the bibliography…
Recommender systems play a significant role in information filtering and have been utilized in different scenarios, such as e-commerce and social media.
Towards Automatic Discovering of Deep Hybrid Network Architecture for Sequential Recommendation. In Proceedings of the ACM Web Conference 2022 (Virtual Event, Lyon, France) (WWW ’22) . Association for Computing Machinery, New York, NY, USA, 1923–1932
Mingyue Cheng, Zhiding Liu, Qi Liu, Shenyang Ge, and Enhong Chen. 2022 · 1932
Earlier work this paper cites.
Asynchronous Methods for Deep Reinforcement Learning. In Proceedings of The 33rd International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 48) , Maria Florina Balcan and Kilian Q. Weinberger (Eds.). PMLR, New York, New York, USA, 1928–1937
Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu. 2016 · 1937
Earlier work this paper cites.
Embedding-Based News Recommendation for Millions of Users. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Halifax, NS, Canada) (KDD ’17) . Association for Computing Machinery, New York, NY, USA, 1933–1942
Shumpei Okura, Yukihiro Tagami, Shingo Ono, and Akira Tajima. 2017 · 1942
Earlier work this paper cites.
AutoCross: Automatic Feature Crossing for Tabular Data in Real-World Applications. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (Anchorage, AK, USA) (KDD ’19) . Association for Computing Machinery, New York, NY, USA, 1936–1945
Yuanfei Luo, Mengshuo Wang, Hao Zhou, Quanming Yao, Wei-Wei Tu, Yuqiang Chen, Wenyuan Dai, and Qiang Yang. 2019 · 1945
Earlier work this paper cites.
Statistical theory of extreme values and some practical applications: a series of lectures . Vol. 33
Emil Julius Gumbel. 1954 · 1954
Earlier work this paper cites.
AutoField: Automating Feature Selection in Deep Recommender Systems. In Proceedings of the ACM Web Conference 2022 (Virtual Event, Lyon, France) (WWW ’22) . Association for Computing Machinery, New York, NY, USA, 1977–1986
Yejing Wang, Xiangyu Zhao, Tong Xu, and Xian Wu. 2022 · 1986
Earlier work this paper cites.
Bayesian variable selection in linear regression
Toby J Mitchell and John J Beauchamp. 1988 · 1988
Earlier work this paper cites.
Hierarchical optimization: An introduction
G Anandalingam and Terry L Friesz. 1992 · 1992
Earlier work this paper cites.
Regression shrinkage and selection via the lasso
Robert Tibshirani. 1996 · 1996
Earlier work this paper cites.
The information bottleneck method
Naftali Tishby, Fernando C Pereira, and William Bialek. 2000 · 2000
Earlier work this paper cites.
Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th international conference on World Wide Web(WWW ’01) . 285–295
Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl. 2001 · 2001
Earlier work this paper cites.
Bayesian factor regression models in the “large p, small n” paradigm
JM Bernardo, MJ Bayarri, JO Berger, AP Dawid, D Heckerman, AFM Smith, and M West. 2003 · 2003
Earlier work this paper cites.
Amazon. com recommendations: Item-to-item collaborative filtering
Greg Linden, Brent Smith, and Jeremy York. 2003 · 2003
Earlier work this paper cites.
Feature selection for high-dimensional data: A fast correlation-based filter solution. In Proceedings of the 20th international conference on machine learning (ICML-03) . 856–863
Lei Yu and Huan Liu. 2003 · 2003
Earlier work this paper cites.
The prediction error in CLS and PLS: the importance of feature selection prior to multivariate calibration
Boaz Nadler and Ronald R Coifman. 2005 · 2005
Earlier work this paper cites.
Improving Recommendation Lists through Topic Diversification. In Proceedings of the 14th International Conference on World Wide Web (Chiba, Japan) (WWW ’05) . Association for Computing Machinery, New York, NY, USA, 22–32
Cai-Nicolas Ziegler, Sean M. McNee, Joseph A. Konstan, and Georg Lausen. 2005 · 2005
Earlier work this paper cites.
Scalable collaborative filtering with jointly derived neighborhood interpolation weights. In Seventh IEEE international conference on data mining (ICDM 2007) . IEEE, 43–52
Robert M Bell and Yehuda Koren. 2007 · 2007
Earlier work this paper cites.
An overview of bilevel optimization
Benoît Colson, Patrice Marcotte, and Gilles Savard. 2007 · 2007
Earlier work this paper cites.
The Long Tail of Recommender Systems and How to Leverage It. In Proceedings of the 2008 ACM Conference on Recommender Systems (Lausanne, Switzerland) (RecSys ’08) . Association for Computing Machinery, New York, NY, USA, 11–18
Yoon-Joo Park and Alexander Tuzhilin. 2008 · 2008
Earlier work this paper cites.
Tensor decompositions and applications
Tamara G Kolda and Brett W Bader. 2009 · 2009
Earlier work this paper cites.
Matrix factorization techniques for recommender systems
Yehuda Koren, Robert Bell, and Chris Volinsky. 2009 · 2009
Earlier work this paper cites.
From ranknet to lambdarank to lambdamart: An overview
Christopher JC Burges. 2010 · 2010
Earlier work this paper cites.
Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (Proceedings of Machine Learning Research, Vol. 9) , Yee Whye Teh and Mike Titterington (Eds.). PMLR, Chia Laguna Resort, Sardinia, Italy, 249–256
Xavier Glorot and Yoshua Bengio. 2010 · 2010
Earlier work this paper cites.
Factorization Machines. In 2010 IEEE International Conference on Data Mining . 995–1000
Steffen Rendle. 2010 · 2010
Earlier work this paper cites.
Submodular Meets Spectral: Greedy Algorithms for Subset Selection, Sparse Approximation and Dictionary Selection. In Proceedings of the 28th International Conference on International Conference on Machine Learning (Bellevue, Washington, USA) (ICML’11) . Omnipress, Madison, WI, USA, 1057–1064
Abhimanyu Das and David Kempe. 2011 · 2011
Earlier work this paper cites.
Performance analysis of various activation functions in generalized MLP architectures of neural networks
Bekir Karlik and A Vehbi Olgac. 2011 · 2011
Earlier work this paper cites.
A Practical Guide to Training Restricted Boltzmann Machines
Geoffrey E. Hinton. 2012 · 2012
Earlier work this paper cites.
Recommender systems
Linyuan Lü, Matúš Medo, Chi Ho Yeung, Yi-Cheng Zhang, Zi-Ke Zhang, and Tao Zhou. 2012 · 2012
Earlier work this paper cites.
BPR: Bayesian personalized ranking from implicit feedback
Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2012 · 2012
Earlier work this paper cites.
Auto-encoding variational bayes
Diederik P Kingma and Max Welling. 2013 · 2013
Earlier work this paper cites.
Practical Lessons from Predicting Clicks on Ads at Facebook. In Proceedings of the Eighth International Workshop on Data Mining for Online Advertising (New York, NY, USA) (ADKDD’14) . Association for Computing Machinery, New York, NY, USA, 1–9
Xinran He, Junfeng Pan, Ou Jin, Tianbing Xu, Bo Liu, Tao Xu, Yanxin Shi, Antoine Atallah, Ralf Herbrich, Stuart Bowers, and Joaquin Quiñonero Candela. 2014 · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2014 · 2014
Earlier work this paper cites.
The MovieLens Datasets: History and Context
F. Maxwell Harper and Joseph A. Konstan. 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.
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. In Proceedings of the 32nd International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 37) , Francis Bach and David Blei (Eds.). PMLR, Lille, France, 448–456
Sergey Ioffe and Christian Szegedy. 2015 · 2015
Earlier work this paper cites.
Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. 2015 · 2015
Earlier work this paper cites.
Online recommendation systems in a B2C E-commerce context: a review and future directions
Seth Siyuan Li and Elena Karahanna. 2015 · 2015
Earlier work this paper cites.
AutoRec: Autoencoders Meet Collaborative Filtering. In Proceedings of the 24th International Conference on World Wide Web (Florence, Italy) (WWW ’15 Companion) . Association for Computing Machinery, New York, NY, USA, 111–112
Suvash Sedhain, Aditya Krishna Menon, Scott Sanner, and Lexing Xie. 2015 · 2015
Earlier work this paper cites.
Collaborative Deep Learning for Recommender Systems. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Sydney, NSW, Australia) (KDD ’15) . Association for Computing Machinery, New York, NY, USA, 1235–1244
Hao Wang, Naiyan Wang, and Dit-Yan Yeung. 2015 · 2015
Earlier work this paper cites.
Social networking meets recommender systems: survey
Guandong Xu, Zhiang Wu, Yanchun Zhang, and Jie Cao. 2015 · 2015
Earlier work this paper cites.
Higher-order factorization machines. In Advances in Neural Information Processing Systems , Vol. 29. 3351–3359
Mathieu Blondel, Akinori Fujino, Naonori Ueda, and Masakazu Ishihata. 2016 · 2016
Earlier work this paper cites.
Wide & Deep Learning for Recommender Systems. In Proceedings of the 1st Workshop on Deep Learning for Recommender Systems (Boston, MA, USA) (DLRS 2016) . Association for Computing Machinery, New York, NY, USA, 7–10
Heng-Tze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, Greg Corrado, Wei Chai, Mustafa Ispir, Rohan Anil, Zakaria Haque, Lichan Hong, Vihan Jain, Xiaobing Liu, and Hemal Shah. 2016 · 2016
Earlier work this paper cites.
Deep Neural Networks for YouTube Recommendations. In Proceedings of the 10th ACM Conference on Recommender Systems (Boston, Massachusetts, USA) (RecSys ’16) . Association for Computing Machinery, New York, NY, USA, 191–198
Paul Covington, Jay Adams, and Emre Sargin. 2016 · 2016
Earlier work this paper cites.
Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition(CVPR’16) . 770–778
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016 · 2016
Earlier work this paper cites.
Categorical reparameterization with gumbel-softmax
Eric Jang, Shixiang Gu, and Ben Poole. 2016 · 2016
Earlier work this paper cites.
Field-Aware Factorization Machines for CTR Prediction. In Proceedings of the 10th ACM Conference on Recommender Systems (Boston, Massachusetts, USA) (RecSys ’16) . Association for Computing Machinery, New York, NY, USA, 43–50
Yuchin Juan, Yong Zhuang, Wei-Sheng Chin, and Chih-Jen Lin. 2016 · 2016
Cited alongside, same era.
Large-Margin Softmax Loss for Convolutional Neural Networks. In Proceedings of The 33rd International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 48) , Maria Florina Balcan and Kilian Q. Weinberger (Eds.). PMLR, New York, New York, USA, 507–516
Weiyang Liu, Yandong Wen, Zhiding Yu, and Meng Yang. 2016 · 2016
Cited alongside, same era.
The concrete distribution: A continuous relaxation of discrete random variables
Chris J Maddison, Andriy Mnih, and Yee Whye Teh. 2016 · 2016
Cited alongside, same era.
Product-based neural networks for user response prediction. In 2016 IEEE 16th International Conference on Data Mining (ICDM’16) . IEEE, 1149–1154
Yanru Qu, Han Cai, Kan Ren, Weinan Zhang, Yong Yu, Ying Wen, and Jun Wang. 2016 · 2016
LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (Virtual Event, China) (SIGIR ’20) . Association for Computing Machinery, New York, NY, USA, 639–648
Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, YongDong Zhang, and Meng Wang. 2020 · 2020
Later among the works it cites.
Neural Input Search for Large Scale Recommendation Models. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (Virtual Event, CA, USA) (KDD ’20) . Association for Computing Machinery, New York, NY, USA, 2387–2397
Manas R. Joglekar, Cong Li, Mei Chen, Taibai Xu, Xiaoming Wang, Jay K. Adams, Pranav Khaitan, Jiahui Liu, and Quoc V. Le. 2020 · 2020
Later among the works it cites.
AutoFeature: Searching for Feature Interactions and Their Architectures for Click-through Rate Prediction. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management (Virtual Event, Ireland) (CIKM ’20) . Association for Computing Machinery, New York, NY, USA, 625–634
Farhan Khawar, Xu Hang, Ruiming Tang, Bin Liu, Zhenguo Li, and Xiuqiang He. 2020 · 2020
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.
"Why Should I Trust You?": Explaining the Predictions of Any Classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (San Francisco, California, USA) (KDD ’16) . Association for Computing Machinery, New York, NY, USA, 1135–1144
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016 · 2016
Cited alongside, same era.
Edge computing: Vision and challenges
Weisong Shi, Jie Cao, Quan Zhang, Youhuizi Li, and Lanyu Xu. 2016 · 2016
Cited alongside, same era.
Deep Learning over Multi-field Categorical Data. In Advances in Information Retrieval , Nicola Ferro, Fabio Crestani, Marie-Francine Moens, Josiane Mothe, Fabrizio Silvestri, Giorgio Maria Di Nunzio, Claudia Hauff, and Gianmaria Silvello (Eds.). Springer International Publishing, Cham, 45–57
Weinan Zhang, Tianming Du, and Jun Wang. 2016 · 2016
Cited alongside, same era.
Neural architecture search with reinforcement learning
Barret Zoph and Quoc V Le. 2016 · 2016
Cited alongside, same era.
Neural architecture search: A survey
Thomas Elsken, Jan Hendrik Metzen, and Frank Hutter. 2019 · 2017
Cited alongside, same era.
DeepFM: A Factorization-Machine Based Neural Network for CTR Prediction. In Proceedings of the 26th International Joint Conference on Artificial Intelligence (Melbourne, Australia) (IJCAI’17) . AAAI Press, 1725–1731
Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, and Xiuqiang He. 2017 · 2017
Cited alongside, same era.
Neural Collaborative Filtering. In Proceedings of the 26th International Conference on World Wide Web (Perth, Australia) (WWW ’17) . International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE, 173–182
Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017 · 2017
Cited alongside, same era.
Hierarchical representations for efficient architecture search
Hanxiao Liu, Karen Simonyan, Oriol Vinyals, Chrisantha Fernando, and Koray Kavukcuoglu. 2017 · 2017
Cited alongside, same era.
NAS-Bench-NLP: neural architecture search benchmark for natural language processing
Nikita Klyuchnikov, Ilya Trofimov, Ekaterina Artemova, Mikhail Salnikov, Maxim Fedorov, and Evgeny Burnaev. 2020 · 2020
Later among the works it cites.
Soft Threshold Weight Reparameterization for Learnable Sparsity. In Proceedings of the 37th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 119) , Hal Daumé III and Aarti Singh (Eds.). PMLR, 5544–5555
Aditya Kusupati, Vivek Ramanujan, Raghav Somani, Mitchell Wortsman, Prateek Jain, Sham Kakade, and Ali Farhadi. 2020 · 2020
Later among the works it cites.
Auto seg-loss: Searching metric surrogates for semantic segmentation
Hao Li, Chenxin Tao, Xizhou Zhu, Xiaogang Wang, Gao Huang, and Jifeng Dai. 2020 · 2020
Later among the works it cites.
Stochastic Loss Function
Qingliang Liu and Jinmei Lai. 2020 · 2020
Later among the works it cites.
Memory augmented graph neural networks for sequential recommendation. In Proceedings of the AAAI conference on artificial intelligence , Vol. 34. 5045–5052
Chen Ma, Liheng Ma, Yingxue Zhang, Jianing Sun, Xue Liu, and Mark Coates. 2020 · 2020
Later among the works it cites.
GAG: Global Attributed Graph Neural Network for Streaming Session-Based Recommendation. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (Virtual Event, China) (SIGIR ’20) . Association for Computing Machinery, New York, NY, USA, 669–678
Ruihong Qiu, Hongzhi Yin, Zi Huang, and Tong Chen. 2020 · 2020
Later among the works it cites.
Compositional Embeddings Using Complementary Partitions for Memory-Efficient Recommendation Systems. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (Virtual Event, CA, USA) (KDD ’20) . Association for Computing Machinery, New York, NY, USA, 165–175
Hao-Jun Michael Shi, Dheevatsa Mudigere, Maxim Naumov, and Jiyan Yang. 2020 · 2020
Later among the works it cites.
Towards Automated Neural Interaction Discovery for Click-Through Rate Prediction. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (Virtual Event, CA, USA) (KDD ’20) . Association for Computing Machinery, New York, NY, USA, 945–955
Qingquan Song, Dehua Cheng, Hanning Zhou, Jiyan Yang, Yuandong Tian, and Xia Hu. 2020 · 2020
Later among the works it cites.
Adaptive Network Alignment with Unsupervised and Multi-order Convolutional Networks. In 2020 IEEE 36th International Conference on Data Engineering (ICDE) . 85–96
Huynh Thanh Trung, Tong Van Vinh, Nguyen Thanh Tam, Hongzhi Yin, Matthias Weidlich, and Nguyen Quoc Viet Hung. 2020 · 2020
Later among the works it cites.
Michael Tsang, Dehua Cheng, Hanpeng Liu, Xue Feng, Eric Zhou, and Yan Liu. 2020 · 2020
Later among the works it cites.
Next Point-of-Interest Recommendation on Resource-Constrained Mobile Devices. In Proceedings of The Web Conference 2020 (Taipei, Taiwan) (WWW ’20) . Association for Computing Machinery, New York, NY, USA, 906–916
Qinyong Wang, Hongzhi Yin, Tong Chen, Zi Huang, Hao Wang, Yanchang Zhao, and Nguyen Quoc Viet Hung. 2020c · 2020
Later among the works it cites.
A practical incremental method to train deep ctr models
Yichao Wang, Huifeng Guo, Ruiming Tang, Zhirong Liu, and Xiuqiang He. 2020a · 2020
Later among the works it cites.
Efficient Neural Interaction Function Search for Collaborative Filtering. In Proceedings of The Web Conference 2020 (Taipei, Taiwan) (WWW ’20) . Association for Computing Machinery, New York, NY, USA, 1660–1670
Quanming Yao, Xiangning Chen, James T. Kwok, Yong Li, and Cho-Jui Hsieh. 2020a · 2020
Later among the works it cites.
Efficient Neural Architecture Search via Proximal Iterations
Quanming Yao, Ju Xu, Wei-Wei Tu, and Zhanxing Zhu. 2020b · 2020
Later among the works it cites.
Fuxictr: An open benchmark for click-through rate prediction
Jieming Zhu, Jinyang Liu, Shuai Yang, Qi Zhang, and Xiuqiang He. 2020 · 2020
Later among the works it cites.
Learning Elastic Embeddings for Customizing On-Device Recommenders. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (Virtual Event, Singapore) (KDD ’21) . Association for Computing Machinery, New York, NY, USA, 138–147
Tong Chen, Hongzhi Yin, Yujia Zheng, Zi Huang, Yang Wang, and Meng Wang. 2021 · 2021
Later among the works it cites.
DeepLight: Deep Lightweight Feature Interactions for Accelerating CTR Predictions in Ad Serving. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining (Virtual Event, Israel) (WSDM ’21) . Association for Computing Machinery, New York, NY, USA, 922–930
Wei Deng, Junwei Pan, Tian Zhou, Deguang Kong, Aaron Flores, and Guang Lin. 2021 · 2021
Later among the works it cites.
Progressive Feature Interaction Search for Deep Sparse Network. In Advances in Neural Information Processing Systems , M. Ranzato, A. Beygelzimer, Y. Dauphin, P.S. Liang, and J. Wortman Vaughan (Eds.), Vol. 34. Curran Associates, Inc., 392–403
Chen Gao, Yinfeng Li, Quanming Yao, Depeng Jin, and Yong Li. 2021 · 2021
Later among the works it cites.
Mixed Dimension Embeddings with Application to Memory-Efficient Recommendation Systems. In 2021 IEEE International Symposium on Information Theory (ISIT) . 2786–2791
A.A. Ginart, Maxim Naumov, Dheevatsa Mudigere, Jiyan Yang, and James Zou. 2021 · 2021
Later among the works it cites.
Lei Guo, Li Tang, Tong Chen, Lei Zhu, Quoc Viet Hung Nguyen, and Hongzhi Yin. 2021 · 2021
Later among the works it cites.
Learnable Embedding Sizes for Recommender Systems
Siyi Liu, Chen Gao, Yihong Chen, Depeng Jin, and Yong Li. 2021 · 2021
Later among the works it cites.
Fuyuan Lyu, Xing Tang, Huifeng Guo, Ruiming Tang, Xiuqiang He, Rui Zhang, and Xue Liu. 2021 · 2021
Later among the works it cites.
A General Method For Automatic Discovery of Powerful Interactions In Click-Through Rate Prediction. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (Virtual Event, Canada) (SIGIR ’21) . Association for Computing Machinery, New York, NY, USA, 1298–1307
Ze Meng, Jinnian Zhang, Yumeng Li, Jiancheng Li, Tanchao Zhu, and Lifeng Sun. 2021 · 2021
Later among the works it cites.
AutoSTG: Neural Architecture Search for Predictions of Spatio-Temporal Graph. In Proceedings of the Web Conference 2021 (Ljubljana, Slovenia) (WWW ’21) . Association for Computing Machinery, New York, NY, USA, 1846–1855
Zheyi Pan, Songyu Ke, Xiaodu Yang, Yuxuan Liang, Yong Yu, Junbo Zhang, and Yu Zheng. 2021 · 2021
Later among the works it cites.
Detecting Beneficial Feature Interactions for Recommender Systems
Yixin Su, Rui Zhang, Sarah Erfani, and Zhenghua Xu. 2021 · 2021
Later among the works it cites.
AutoIAS: Automatic Integrated Architecture Searcher for Click-Trough Rate Prediction. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management (Virtual Event, Queensland, Australia) (CIKM ’21) . Association for Computing Machinery, New York, NY, USA, 2101–2110
Zhikun Wei, Xin Wang, and Wenwu Zhu. 2021 · 2021
Later among the works it cites.
FIVES: Feature Interaction Via Edge Search for Large-Scale Tabular Data. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (Virtual Event, Singapore) (KDD ’21) . Association for Computing Machinery, New York, NY, USA, 3795–3805
Yuexiang Xie, Zhen Wang, Yaliang Li, Bolin Ding, Nezihe Merve Gürel, Ce Zhang, Minlie Huang, Wei Lin, and Jingren Zhou. 2021 · 2021
Later among the works it cites.
Learning Effective and Efficient Embedding via an Adaptively-Masked Twins-based Layer
Bencheng Yan, Pengjie Wang, Kai Zhang, Wei Lin, Kuang-Chih Lee, Jian Xu, and Bo Zheng. 2021 · 2021
Later among the works it cites.
Medmnist classification decathlon: A lightweight automl benchmark for medical image analysis. In 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI) . IEEE, 191–195
Jiancheng Yang, Rui Shi, and Bingbing Ni. 2021 · 2021
Later among the works it cites.
Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation. In Proceedings of the Web Conference 2021 (Ljubljana, Slovenia) (WWW ’21) . Association for Computing Machinery, New York, NY, USA, 413–424
Junliang Yu, Hongzhi Yin, Jundong Li, Qinyong Wang, Nguyen Quoc Viet Hung, and Xiangliang Zhang. 2021 · 2021
Later among the works it cites.
AutoDim: Field-Aware Embedding Dimension Searchin Recommender Systems. In Proceedings of the Web Conference 2021 (Ljubljana, Slovenia) (WWW ’21) . Association for Computing Machinery, New York, NY, USA, 3015–3022
Xiangyu Zhao, Haochen Liu, Hui Liu, Jiliang Tang, Weiwei Guo, Jun Shi, Sida Wang, Huiji Gao, and Bo Long. 2021b · 2021
Later among the works it cites.
AutoEmb: Automated Embedding Dimensionality Search in Streaming Recommendations. In 2021 IEEE International Conference on Data Mining (ICDM) . 896–905
Xiangyu Zhaok, Haochen Liu, Wenqi Fan, Hui Liu, Jiliang Tang, Chong Wang, Ming Chen, Xudong Zheng, Xiaobing Liu, and Xiwang Yang. 2021 · 2021
Later among the works it cites.
Open Benchmarking for Click-Through Rate Prediction. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management (Virtual Event, Queensland, Australia) (CIKM ’21) . Association for Computing Machinery, New York, NY, USA, 2759–2769
Jieming Zhu, Jinyang Liu, Shuai Yang, Qi Zhang, and Xiuqiang He. 2021 · 2021
Later among the works it cites.
Automated Machine Learning for Deep Recommender Systems: A Survey
Bo Chen, Xiangyu Zhao, Yejing Wang, Wenqi Fan, Huifeng Guo, and Ruiming Tang. 2022a · 2022
Closest in time.
AutoMARS: Searching to Compress Multi-Modality Recommendation Systems. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management (Atlanta, GA, USA) (CIKM ’22) . Association for Computing Machinery, New York, NY, USA, 727–736
Duc Hoang, Haotao Wang, Handong Zhao, Ryan Rossi, Sungchul Kim, Kanak Mahadik, and Zhangyang Wang. 2022 · 2022
Closest in time.
Advances in Collaborative Filtering
Yehuda Koren, Steffen Rendle, and Robert Bell. 2022 · 2022
Closest in time.
AdaFS: Adaptive Feature Selection in Deep Recommender System. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (Washington DC, USA) (KDD ’22) . Association for Computing Machinery, New York, NY, USA, 3309–3317
Weilin Lin, Xiangyu Zhao, Yejing Wang, Tong Xu, and Xian Wu. 2022 · 2022
Closest in time.
Decentralized Collaborative Learning Framework for Next POI Recommendation
Jing Long, Tong Chen, Nguyen Quoc Viet Hung, and Hongzhi Yin. 2022 · 2022
Closest in time.
Single-shot Embedding Dimension Search in Recommender System
Liang Qu, Yonghong Ye, Ningzhi Tang, Lixin Zhang, Yuhui Shi, and Hongzhi Yin. 2022 · 2022
Closest in time.
Detecting Arbitrary Order Beneficial Feature Interactions for Recommender Systems. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (Washington DC, USA) (KDD ’22) . Association for Computing Machinery, New York, NY, USA, 1676–1686
Yixin Su, Yunxiang Zhao, Sarah Erfani, Junhao Gan, and Rui Zhang. 2022 · 2022
Closest in time.
Graph Neural Networks in Recommender Systems: A Survey
Shiwen Wu, Fei Sun, Wentao Zhang, Xu Xie, and Bin Cui. 2022 · 2022
Closest in time.
On-Device Next-Item Recommendation with Self-Supervised Knowledge Distillation. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (Madrid, Spain) (SIGIR ’22) . Association for Computing Machinery, New York, NY, USA, 546–555
Xin Xia, Hongzhi Yin, Junliang Yu, Qinyong Wang, Guandong Xu, and Quoc Viet Hung Nguyen. 2022 · 2022
Closest in time.