Fetching the paper…
Reading the bibliography…
Combinatorial features are essential for the success of many commercial models.
Maximum-margin matrix factorization. In Advances in neural information processing systems . 1329–1336
Nathan Srebro, Jason Rennie, and Tommi S Jaakkola. 2005 · 2005
Earlier work this paper cites.
Predicting clicks: estimating the click-through rate for new ads. In Proceedings of the 16th international conference on World Wide Web . ACM, 521–530
Matthew Richardson, Ewa Dominowska, and Robert Ragno. 2007 · 2007
Earlier work this paper cites.
Factorization meets the neighborhood: a multifaceted collaborative filtering model. In Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining . ACM, 426–434
Yehuda Koren. 2008 · 2008
Earlier work this paper cites.
One-class collaborative filtering. In Data Mining, 2008. ICDM’08. Eighth IEEE International Conference on . IEEE, 502–511
Rong Pan, Yunhong Zhou, Bin Cao, Nathan N Liu, Rajan Lukose, Martin Scholz, and Qiang Yang. 2008 · 2008
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.
BPR: Bayesian personalized ranking from implicit feedback. In Proceedings of the twenty-fifth conference on uncertainty in artificial intelligence . AUAI Press, 452–461
Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009 · 2009
Earlier work this paper cites.
A log-linear model with latent features for dyadic prediction. In Data Mining (ICDM), 2010 IEEE 10th International Conference on . IEEE, 364–373
Aditya Krishna Menon and Charles Elkan. 2010 · 2010
Earlier work this paper cites.
Recurrent neural network based language model. In Eleventh Annual Conference of the International Speech Communication Association
Tomáš Mikolov, Martin Karafiát, Lukáš Burget, Jan Černockỳ, and Sanjeev Khudanpur. 2010 · 2010
Earlier work this paper cites.
Factorization machines. In Data Mining (ICDM), 2010 IEEE 10th International Conference on . IEEE, 995–1000
Steffen Rendle. 2010 · 2010
Earlier work this paper cites.
Pairwise interaction tensor factorization for personalized tag recommendation. In Proceedings of the third ACM international conference on Web search and data mining . ACM, 81–90
Steffen Rendle and Lars Schmidt-Thieme. 2010 · 2010
Earlier work this paper cites.
SVDFeature: a toolkit for feature-based collaborative filtering
Tianqi Chen, Weinan Zhang, Qiuxia Lu, Kailong Chen, Zhao Zheng, and Yong Yu. 2012 · 2012
Earlier work this paper cites.
Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups
Geoffrey Hinton, Li Deng, Dong Yu, George E Dahl, Abdel-rahman Mohamed, Navdeep Jaitly, Andrew Senior, Vincent Vanhoucke, Patrick Nguyen, Tara N Sainath, et al · 2012
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems . 1097–1105
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. 2012 · 2012
Earlier work this paper cites.
Local low-rank matrix approximation. In International Conference on Machine Learning . 82–90
Joonseok Lee, Seungyeon Kim, Guy Lebanon, and Yoram Singer. 2013 · 2013
Earlier work this paper cites.
Ad click prediction: a view from the trenches. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining . ACM, 1222–1230
H Brendan McMahan, Gary Holt, David Sculley, Michael Young, Dietmar Ebner, Julian Grady, Lan Nie, Todd Phillips, Eugene Davydov, Daniel Golovin, et al · 2013
Earlier work this paper cites.
Learning phrase representations using RNN encoder-decoder for statistical machine translation
Kyunghyun Cho, Bart Van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014 · 2014
Cited alongside, same era.
Practical lessons from predicting clicks on ads at facebook. In Proceedings of the Eighth International Workshop on Data Mining for Online Advertising . ACM, 1–9
Xinran He, Junfeng Pan, Ou Jin, Tianbing Xu, Bo Liu, Tao Xu, Yanxin Shi, Antoine Atallah, Ralf Herbrich, Stuart Bowers, et al · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2014 · 2014
Cited alongside, same era.
Improving content-based and hybrid music recommendation using deep learning. In Proceedings of the 22nd ACM international conference on Multimedia . ACM, 627–636
Xinxi Wang and Ye Wang. 2014 · 2014
Cited alongside, same era.
Product-based neural networks for user response prediction. In Data Mining (ICDM), 2016 IEEE 16th International Conference on . IEEE, 1149–1154
Yanru Qu, Han Cai, Kan Ren, Weinan Zhang, Yong Yu, Ying Wen, and Jun Wang. 2016 · 2016
Later among the works it cites.
Deep crossing: Web-scale modeling without manually crafted combinatorial features. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . ACM, 255–262
Ying Shan, T Ryan Hoens, Jian Jiao, Haijing Wang, Dong Yu, and JC Mao. 2016 · 2016
Later among the works it cites.
Collaborative denoising auto-encoders for top-n recommender systems. In Proceedings of the Ninth ACM International Conference on Web Search and Data Mining . ACM, 153–162
Yao Wu, Christopher DuBois, Alice X Zheng, and Martin Ester. 2016 · 2016
Later among the works it cites.
Lambdafm: learning optimal ranking with factorization machines using lambda surrogates. In Proceedings of the 25th ACM International on Conference on Information and Knowledge Management . ACM, 227–236
Fajie Yuan, Guibing Guo, Joemon M Jose, Long Chen, Haitao Yu, and Weinan Zhang. 2016 · 2016
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
A multi-view deep learning approach for cross domain user modeling in recommendation systems. In Proceedings of the 24th International Conference on World Wide Web . International World Wide Web Conferences Steering Committee, 278–288
Ali Mamdouh Elkahky, Yang Song, and Xiaodong He. 2015 · 2015
Cited alongside, same era.
Autorec: Autoencoders meet collaborative filtering. In Proceedings of the 24th International Conference on World Wide Web . ACM, 111–112
Suvash Sedhain, Aditya Krishna Menon, Scott Sanner, and Lexing Xie. 2015 · 2015
Cited alongside, same era.
Collaborative deep learning for recommender systems. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . ACM, 1235–1244
Hao Wang, Naiyan Wang, and Dit-Yan Yeung. 2015 · 2015
Cited alongside, same era.
Deep speech 2: End-to-end speech recognition in english and mandarin. In International Conference on Machine Learning . 173–182
Dario Amodei, Sundaram Ananthanarayanan, Rishita Anubhai, Jingliang Bai, Eric Battenberg, Carl Case, Jared Casper, Bryan Catanzaro, Qiang Cheng, Guoliang Chen, et al · 2016
Cited alongside, same era.
Higher-order factorization machines. In Advances in Neural Information Processing Systems . 3351–3359
Mathieu Blondel, Akinori Fujino, Naonori Ueda, and Masakazu Ishihata. 2016 · 2016
Cited alongside, same era.
Wide & deep learning for recommender systems. In Proceedings of the 1st Workshop on Deep Learning for Recommender Systems . ACM, 7–10
Heng-Tze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, Greg Corrado, Wei Chai, Mustafa Ispir, et al · 2016
Cited alongside, same era.
Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition . 770–778
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016 · 2016
Cited alongside, same era.
VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback. In AAAI . 144–150
Ruining He and Julian McAuley. 2016 · 2016
Cited alongside, same era.
Later among the works it cites.
Attentive collaborative filtering: Multimedia recommendation with item-and component-level attention. In Proceedings of the 40th International ACM SIGIR conference on Research and Development in Information Retrieval . ACM, 335–344
Jingyuan Chen, Hanwang Zhang, Xiangnan He, Liqiang Nie, Wei Liu, and Tat-Seng Chua. 2017 · 2017
Later among the works it cites.
A Hybrid Collaborative Filtering Model with Deep Structure for Recommender Systems. In AAAI . 1309–1315
Xin Dong, Lei Yu, Zhonghuo Wu, Yuxia Sun, Lingfeng Yuan, and Fangxi Zhang. 2017 · 2017
Later among the works it cites.
Deepfm: A factorization-machine based neural network for CTR prediction
Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, and Xiuqiang He. 2017 · 2017
Later among the works it cites.
Neural factorization machines for sparse predictive analytics. In Proceedings of the 40th International ACM SIGIR conference on Research and Development in Information Retrieval . ACM, 355–364
Xiangnan He and Tat-Seng Chua. 2017 · 2017
Later among the works it cites.
Neural collaborative filtering. In Proceedings of the 26th International Conference on World Wide Web . International World Wide Web Conferences Steering Committee, 173–182
Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017 · 2017
Later among the works it cites.
Practical Lessons for Job Recommendations in the Cold-Start Scenario. In Proceedings of the Recommender Systems Challenge 2017 (RecSys Challenge ’17) . ACM, New York, NY, USA, Article 4, 6 pages
Jianxun Lian, Fuzheng Zhang, Min Hou, Hongwei Wang, Xing Xie, and Guangzhong Sun. 2017a · 2017
Later among the works it cites.
Model Ensemble for Click Prediction in Bing Search Ads. In Proceedings of the 26th International Conference on World Wide Web Companion . International World Wide Web Conferences Steering Committee, 689–698
Xiaoliang Ling, Weiwei Deng, Chen Gu, Hucheng Zhou, Cui Li, and Feng Sun. 2017 · 2017
Later among the works it cites.
Deep & Cross Network for Ad Click Predictions
Ruoxi Wang, Bin Fu, Gang Fu, and Mingliang Wang. 2017 · 2017
Later among the works it cites.
Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks. In Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI 2017, Melbourne, Australia, August 19-25, 2017 . 3119–3125
Jun Xiao, Hao Ye, Xiangnan He, Hanwang Zhang, Fei Wu, and Tat-Seng Chua. 2017 · 2017
Later among the works it cites.
Deep interest network for click-through rate prediction
Guorui Zhou, Chengru Song, Xiaoqiang Zhu, Xiao Ma, Yanghui Yan, Xingya Dai, Han Zhu, Junqi Jin, Han Li, and Kun Gai. 2017 · 2017
Later among the works it cites.