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Learning a good representation of text is key to many recommendation applications.
Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey E Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014 · 1958
Earlier work this paper cites.
Text categorization with support vector machines: Learning with many relevant features. In European conference on machine learning
Thorsten Joachims. 1998 · 1998
Earlier work this paper cites.
Thumbs up?: sentiment classification using machine learning techniques. In Proceedings of the ACL-02 conference on Empirical methods in natural language processing-Volume 10
Bo Pang, Lillian Lee, and Shivakumar Vaithyanathan. 2002 · 2002
Earlier work this paper cites.
Regularized multi–task learning. In Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Theodoros Evgeniou and Massimiliano Pontil. 2004 · 2004
Earlier work this paper cites.
Content-based recommendation systems
Michael J Pazzani and Daniel Billsus. 2007 · 2007
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.
Factorization meets the neighborhood: a multifaceted collaborative filtering model. In Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Yehuda Koren. 2008 · 2008
Earlier work this paper cites.
Visualizing data using t-SNE
Laurens van der Maaten and Geoffrey Hinton. 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.
Matrix factorization techniques for recommender systems
Yehuda Koren, Robert Bell, Chris Volinsky, and others. 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
Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009 · 2009
Earlier work this paper cites.
Factorization machines. In 2010 IEEE International Conference on Data Mining
Steffen Rendle. 2010 · 2010
Cited alongside, same era.
Natural language processing (almost) from scratch
Ronan Collobert, Jason Weston, Léon Bottou, Michael Karlen, Koray Kavukcuoglu, and Pavel Kuksa. 2011 · 2011
Cited alongside, same era.
Probabilistic matrix factorization. In NIPS
Ruslan Salakhutdinov and Andriy Mnih. 2011 · 2011
Cited alongside, same era.
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
Cited alongside, same era.
SVDFeature: a toolkit for feature-based collaborative filtering
Tianqi Chen, Weinan Zhang, Qiuxia Lu, Kailong Chen, Zhao Zheng, and Yong Yu. 2012 · 2012
Cited alongside, same era.
Efficient estimation of word representations in vector space
Adam: A Method for Stochastic Optimization
Diederik P Kingma and Jimmy Lei Ba. 2015 · 2015
Later among the works it cites.
Autorec: Autoencoders meet collaborative filtering. In Proceedings of the 24th International Conference on World Wide Web
Suvash Sedhain, Aditya Krishna Menon, Scott Sanner, and Lexing Xie. 2015 · 2015
Later among the works it cites.
Document modeling with gated recurrent neural network for sentiment classification. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing
Duyu Tang, Bing Qin, and Ting Liu. 2015 · 2015
Later among the works it cites.
Collaborative deep learning for recommender systems. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Hao Wang, Naiyan Wang, and Dit-Yan Yeung. 2015 · 2015
Later among the works it cites.
Character-level convolutional networks for text classification. In Advances in Neural Information Processing Systems
Xiang Zhang, Junbo Zhao, and Yann LeCun. 2015 · 2015
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Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013 · 2013
Cited alongside, same era.
Distributed representations of words and phrases and their compositionality
T Mikolov and J Dean. 2013 · 2013
Cited alongside, same era.
Content-based recommendations with poisson factorization. In Advances in Neural Information Processing Systems
Prem K Gopalan, Laurent Charlin, and David Blei. 2014 · 2014
Cited alongside, same era.
Effective use of word order for text categorization with convolutional neural networks
Rie Johnson and Tong Zhang. 2014 · 2014
Cited alongside, same era.
Convolutional neural networks for sentence classification
Yoon Kim. 2014 · 2014
Cited alongside, same era.
Distributed Representations of Sentences and Documents.. In ICML
Quoc V Le and Tomas Mikolov. 2014 · 2014
Cited alongside, same era.
Later among the works it cites.
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
Later among the works it cites.
Entity Embedding-based Anomaly Detection for Heterogeneous Categorical Events. In Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI’16)
Ting Chen, Lu-An Tang, Yizhou Sun, Zhengzhang Chen, and Kai Zhang. 2016 · 2016
Later among the works it cites.
Bag of Tricks for Efficient Text Classification
Armand Joulin, Edouard Grave, Piotr Bojanowski, and Tomas Mikolov. 2016 · 2016
Later among the works it cites.
A Neural Autoregressive Approach to Collaborative Filtering
Yin Zheng, Bangsheng Tang, Wenkui Ding, and Hanning Zhou. 2016 · 2016
Later among the works it cites.
Task-Guided and Path-Augmented Heterogeneous Network Embedding for Author Identification. In Proceedings of the Tenth ACM International Conference on Web Search and Data Mining
Ting Chen and Yizhou Sun. 2017 · 2017
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On Sampling Strategies for Neural Network-based Collaborative Filtering. In Proceedings of the 23th ACM SIGKDD international conference on Knowledge discovery and data mining
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