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In this paper, we study the problem of modeling users' diverse interests.
Dropout: A simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014 · 1958
Earlier work this paper cites.
Learning representations by back-propagating errors
David E Rumelhart, Geoffrey E Hinton, Ronald J Williams, et al · 1988
Earlier work this paper cites.
Handwritten digit recognition with a back-propagation network. In Advances in neural information processing systems
Yann LeCun, Bernhard E Boser, John S Denker, Donnie Henderson, Richard E Howard, Wayne E Hubbard, and Lawrence D Jackel. 1990 · 1990
Earlier work this paper cites.
Recommender systems in e-commerce. In Proceedings of the 1st ACM conference on Electronic commerce
J Ben Schafer, Joseph Konstan, and John Riedl. 1999 · 1999
Earlier work this paper cites.
A fast learning algorithm for deep belief nets
Geoffrey Hinton, Simon Osindero, and Yee-Whye Teh. 2006 · 2006
Earlier work this paper cites.
Restricted Boltzmann machines for collaborative filtering. In Proceedings of the 24th international conference on Machine learning
Ruslan Salakhutdinov, Andriy Mnih, and Geoffrey Hinton. 2007 · 2007
Earlier work this paper cites.
Collaborative filtering for implicit feedback datasets. In 2008 Eighth IEEE International Conference on Data Mining
Yifan Hu, Yehuda Koren, and Chris Volinsky. 2008 · 2008
Earlier work this paper cites.
Relational learning via collective matrix factorization. In Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Ajit P Singh and Geoffrey J Gordon. 2008 · 2008
Earlier work this paper cites.
BPR: Bayesian personalized ranking from implicit feedback. In Proceedings of the twenty-fifth conference on uncertainty in artificial intelligence
Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009 · 2009
Earlier work this paper cites.
A practical guide to training restricted Boltzmann machines
Geoffrey Hinton. 2010 · 2010
Earlier work this paper cites.
Recommender systems: an introduction
Dietmar Jannach, Markus Zanker, Alexander Felfernig, and Gerhard Friedrich. 2010 · 2010
Earlier work this paper cites.
Rectified linear units improve restricted boltzmann machines. In Proceedings of the 27th International Conference on Machine Learning (ICML-10)
Vinod Nair and Geoffrey E Hinton. 2010 · 2010
Earlier work this paper cites.
Probabilistic matrix factorization. In NIPS
Ruslan Salakhutdinov and Andriy Mnih. 2011 · 2011
Earlier work this paper cites.
Collaborative topic modeling for recommending scientific articles. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Chong Wang and David M Blei. 2011 · 2011
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. 2012 · 2012
Earlier work this paper cites.
Factorization Machines with libFM
Steffen Rendle. 2012 · 2012
Earlier work this paper cites.
Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude
Tijmen Tieleman and Geoffrey Hinton. 2012 · 2012
Cited alongside, same era.
Deep content-based music recommendation. In Advances in Neural Information Processing Systems
Aaron Van den Oord, Sander Dieleman, and Benjamin Schrauwen. 2013 · 2013
Cited alongside, same era.
Improving content-based and hybrid music recommendation using deep learning. In Proceedings of the ACM International Conference on Multimedia
Xinxi Wang and Ye Wang. 2014 · 2014
Cited alongside, same era.
Image-based recommendations on styles and substitutes. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval
Julian McAuley, Christopher Targett, Qinfeng Shi, and Anton Van Den Hengel. 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
Neural Factorization Machines for Sparse Predictive Analytics. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, Shinjuku, Tokyo, Japan, August 7-11, 2017
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
Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017 · 2017
Later among the works it cites.
Neural Rating Regression with Abstractive Tips Generation for Recommendation
Piji Li, Zihao Wang, Zhaochun Ren, Lidong Bing, and Wai Lam. 2017 · 2017
Later among the works it cites.
Modeling User Session and Intent with an Attention-based Encoder-Decoder Architecture. In Proceedings of the Eleventh ACM Conference on Recommender Systems
Pablo Loyola, Chen Liu, and Yu Hirate. 2017 · 2017
Later among the works it cites.
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Hao Wang, Naiyan Wang, and Dit-Yan Yeung. 2015 · 2015
Cited alongside, same era.
Ask the gru: Multi-task learning for deep text recommendations. In Proceedings of the 10th ACM Conference on Recommender Systems
Trapit Bansal, David Belanger, and Andrew McCallum. 2016 · 2016
Cited alongside, same era.
Wide & deep learning for recommender systems. In Proceedings of the 1st Workshop on Deep Learning for Recommender Systems
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 neural networks for youtube recommendations. In Proceedings of the 10th ACM Conference on Recommender Systems
Paul Covington, Jay Adams, and Emre Sargin. 2016 · 2016
Cited alongside, same era.
Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering. In Proceedings of the 25th International Conference on World Wide Web
Ruining He and Julian McAuley. 2016 · 2016
Cited alongside, same era.
Convolutional matrix factorization for document context-aware recommendation. In Proceedings of the 10th ACM Conference on Recommender Systems
Donghyun Kim, Chanyoung Park, Jinoh Oh, Sungyoung Lee, and Hwanjo Yu. 2016 · 2016
Cited alongside, same era.
Collaborative recurrent autoencoder: Recommend while learning to fill in the blanks. In Advances in Neural Information Processing Systems
Hao Wang, SHI Xingjian, and Dit-Yan Yeung. 2016 · 2016
Cited alongside, same era.
Collaborative denoising auto-encoders for top-n recommender systems. In Proceedings of the Ninth ACM International Conference on Web Search and Data Mining
Yao Wu, Christopher DuBois, Alice X Zheng, and Martin Ester. 2016 · 2016
Cited alongside, same era.
Sungyong Seo, Jing Huang, Hao Yang, and Yan Liu. 2017 · 2017
Later among the works it cites.
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI 2017, Melbourne, Australia, August 19-25, 2017
Carles Sierra (Ed.). 2017 · 2017
Later among the works it cites.
Jun Xiao, Hao Ye, Xiangnan He, Hanwang Zhang, Fei Wu, and Tat-Seng Chua. 2017 · 2017
Later among the works it cites.
Deep Learning based Recommender System: A Survey and New Perspectives
Shuai Zhang, Lina Yao, and Aixin Sun. 2017b · 2017
Later among the works it cites.
Joint representation learning for top-n recommendation with heterogeneous information sources
Yongfeng Zhang, Qingyao Ai, Xu Chen, and W Croft. 2017a · 2017
Later among the works it cites.
Joint Deep Modeling of Users and Items Using Reviews for Recommendation. In Proceedings of the Tenth ACM International Conference on Web Search and Data Mining
Lei Zheng, Vahid Noroozi, and Philip S Yu. 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.
Neural Attentional Rating Regression with Review-level Explanations. In Proceedings of the 2018 World Wide Web Conference on World Wide Web
Chong Chen, Min Zhang, Yiqun Liu, and Shaoping Ma. 2018 · 2018
Closest in time.
Learning from Multi-View Multi-Way Data via Structural Factorization Machines. In Proceedings of the 2018 World Wide Web Conference on World Wide Web
Chun-Ta Lu, Lifang He, Hao Ding, Bokai Cao, and S Yu Philip. 2018 · 2018
Closest in time.
Attention-based Group Recommendation
Tran Dang Quang Vinh, Tuan-Anh Nguyen Pham, Gao Cong, and Xiao-Li Li. 2018 · 2018
Closest in time.
Dynamic attention deep model for article recommendation by learning human editors’ demonstration. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Xuejian Wang, Lantao Yu, Kan Ren, Guanyu Tao, Weinan Zhang, Yong Yu, and Jun Wang. 2017b · 2059
Closest in time.