Fetching the paper…
Reading the bibliography…
Model-based methods for recommender systems have been studied extensively for years.
Flat minima
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
Earlier work this paper cites.
Searching in metric spaces by spatial approximation
Gonzalo Navarro. 2002 · 2002
Earlier work this paper cites.
Fast nearest neighbor retrieval for bregman divergences. In Proceedings of the 25th international conference on Machine learning . 112–119
Lawrence Cayton. 2008 · 2008
Earlier work this paper cites.
Noise-contrastive estimation: A new estimation principle for unnormalized statistical models. In Proceedings of the thirteenth international conference on artificial intelligence and statistics . JMLR Workshop and Conference Proceedings, 297–304
Michael Gutmann and Aapo Hyvärinen. 2010 · 2010
Earlier work this paper cites.
Maximum inner-product search using cone trees. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining . 931–939
Parikshit Ram and Alexander G Gray. 2012 · 2012
Earlier work this paper cites.
Fast exact max-kernel search. In Proceedings of the 2013 SIAM International Conference on Data Mining . SIAM, 1–9
Ryan R Curtin, Parikshit Ram, and Alexander G Gray. 2013 · 2013
Earlier work this paper cites.
Speeding up the xbox recommender system using a euclidean transformation for inner-product spaces. In Proceedings of the 8th ACM Conference on Recommender systems . 257–264
Yoram Bachrach, Yehuda Finkelstein, Ran Gilad-Bachrach, Liran Katzir, Noam Koenigstein, Nir Nice, and Ulrich Paquet. 2014 · 2014
Earlier work this paper cites.
Dual-tree fast exact max-kernel search
Ryan R Curtin and Parikshit Ram. 2014 · 2014
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.
Approximate nearest neighbor algorithm based on navigable small world graphs
Yury Malkov, Alexander Ponomarenko, Andrey Logvinov, and Vladimir Krylov. 2014 · 2014
Earlier work this paper cites.
Asymmetric LSH (ALSH) for sublinear time maximum inner product search (MIPS)
Anshumali Shrivastava and Ping Li. 2014 · 2014
Earlier work this paper cites.
Natural neural networks
Guillaume Desjardins, Karen Simonyan, Razvan Pascanu, et al · 2015
Earlier work this paper cites.
Asymmetric minwise hashing for indexing binary inner products and set containment. In Proceedings of the 24th international conference on world wide web . 981–991
Anshumali Shrivastava and Ping Li. 2015 · 2015
Cited alongside, same era.
Tensorflow: Large-scale machine learning on heterogeneous distributed systems
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, et al · 2016
Cited alongside, same era.
Categorical reparameterization with gumbel-softmax
Eric Jang, Shixiang Gu, and Ben Poole. 2016 · 2016
Cited alongside, same era.
Neural collaborative filtering. In Proceedings of the 26th international conference on world wide web . 173–182
Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017 · 2017
Cited alongside, same era.
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
Later among the works it cites.
Relevance Proximity Graphs for Fast Relevance Retrieval
Stanislav Morozov and Artem Babenko. 2019 · 2019
Later among the works it cites.
Adversarial training for free!
Ali Shafahi, Mahyar Najibi, Amin Ghiasi, Zheng Xu, John Dickerson, Christoph Studer, Larry S Davis, Gavin Taylor, and Tom Goldstein. 2019 · 2019
Later among the works it cites.
Adversarial examples: Attacks and defenses for deep learning
Xiaoyong Yuan, Pan He, Qile Zhu, and Xiaolin Li. 2019 · 2019
Later among the works it cites.
Joint optimization of tree-based index and deep model for recommender systems
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer, and Tom Goldstein. 2017 · 2017
Cited alongside, same era.
Learning from simulated and unsupervised images through adversarial training. In Proceedings of the IEEE conference on computer vision and pattern recognition . 2107–2116
Ashish Shrivastava, Tomas Pfister, Oncel Tuzel, Joshua Susskind, Wenda Wang, and Russell Webb. 2017 · 2017
Cited alongside, same era.
Efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs
Yu A Malkov and Dmitry A Yashunin. 2018 · 2018
Cited alongside, same era.
Gradient adversarial training of neural networks
Ayan Sinha, Zhao Chen, Vijay Badrinarayanan, and Andrew Rabinovich. 2018 · 2018
Cited alongside, same era.
Hessian-based analysis of large batch training and robustness to adversaries
Zhewei Yao, Amir Gholami, Qi Lei, Kurt Keutzer, and Michael W Mahoney. 2018 · 2018
Cited alongside, same era.
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
Cited alongside, same era.
Learning tree-based deep model for recommender systems. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 1079–1088
Han Zhu, Xiang Li, Pengye Zhang, Guozheng Li, Jie He, Han Li, and Kun Gai. 2018 · 2018
Cited alongside, same era.
Entropy-sgd: Biasing gradient descent into wide valleys
Pratik Chaudhari, Anna Choromanska, Stefano Soatto, Yann LeCun, Carlo Baldassi, Christian Borgs, Jennifer Chayes, Levent Sagun, and Riccardo Zecchina. 2019 · 2019
Cited alongside, same era.
Han Zhu, Daqing Chang, Ziru Xu, Pengye Zhang, Xiang Li, Jie He, Han Li, Jian Xu, and Kun Gai. 2019 · 2019
Later among the works it cites.
Deep Retrieval: Learning A Retrievable Structure for Large-Scale Recommendations
Weihao Gao, Xiangjun Fan, Chong Wang, Jiankai Sun, Kai Jia, Wenzhi Xiao, Ruofan Ding, Xingyan Bin, Hui Yang, and Xiaobing Liu. 2020 · 2020
Later among the works it cites.
Embedding-based retrieval in facebook search. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 2553–2561
Jui-Ting Huang, Ashish Sharma, Shuying Sun, Li Xia, David Zhang, Philip Pronin, Janani Padmanabhan, Giuseppe Ottaviano, and Linjun Yang. 2020 · 2020
Later among the works it cites.
Search-based user interest modeling with lifelong sequential behavior data for click-through rate prediction. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management . 2685–2692
Qi Pi, Guorui Zhou, Yujing Zhang, Zhe Wang, Lejian Ren, Ying Fan, Xiaoqiang Zhu, and Kun Gai. 2020 · 2020
Later among the works it cites.
Fast item ranking under neural network based measures. In Proceedings of the 13th International Conference on Web Search and Data Mining . 591–599
Shulong Tan, Zhixin Zhou, Zhaozhuo Xu, and Ping Li. 2020 · 2020
Later among the works it cites.
Learning optimal tree models under beam search. In International Conference on Machine Learning . PMLR, 11650–11659
Jingwei Zhuo, Ziru Xu, Wei Dai, Han Zhu, Han Li, Jian Xu, and Kun Gai. 2020 · 2020
Later among the works it cites.
On the convergence and robustness of adversarial training
Yisen Wang, Xingjun Ma, James Bailey, Jinfeng Yi, Bowen Zhou, and Quanquan Gu. 2021 · 2021
Later among the works it cites.