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Recommender systems are aimed at generating a personalized ranked list of items that an end user might be interested in.
Item-based Collaborative Filtering Recommendation Algorithms. In Proceedings of the 10th International Conference on World Wide Web
Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl. 2001 · 2001
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Learning to rank using gradient descent. In Proceedings of the 22nd International Conference on Machine Learning
Chris Burges, Tal Shaked, Erin Renshaw, Ari Lazier, Matt Deeds, Nicole Hamilton, and Greg Hullender. 2005 · 2005
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Probabilistic Matrix Factorization. In Proceedings of the 20th International Conference on Neural Information Processing Systems
Ruslan Salakhutdinov and Andriy Mnih. 2007 · 2007
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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
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Collaborative Filtering for Implicit Feedback Datasets. In Proceedings of the 8th IEEE International Conference on Data Mining
Yifan Hu, Yehuda Koren, and Chris Volinsky. 2008 · 2008
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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
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BPR: Bayesian Personalized Ranking from Implicit Feedback. In Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence
Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009 · 2009
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Performance of Recommender Algorithms on Top-n Recommendation Tasks. In Proceedings of the 4th ACM Conference on Recommender Systems
Paolo Cremonesi, Yehuda Koren, and Roberto Turrin. 2010 · 2010
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Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion
Pascal Vincent, Hugo Larochelle, Isabelle Lajoie, Yoshua Bengio, and Pierre-Antoine Manzagol. 2010 · 2010
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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
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Collaborative ranking. In Proceedings of the 5th ACM International Conference on Web Search and Data Mining
Suhrid Balakrishnan and Sumit Chopra. 2012 · 2012
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Multimodal Learning with Deep Boltzmann Machines
Nitish Srivastava and Ruslan R Salakhutdinov. 2012 · 2012
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Metric learning: A survey
Brian Kulis et al · 2013
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Reasoning with Neural Tensor Networks for Knowledge Base Completion. In Advances in Neural Information Processing Systems
Richard Socher, Danqi Chen, Christopher D Manning, and Andrew Ng. 2013 · 2013
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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
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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
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Fast matrix factorization for online recommendation with implicit feedback. In Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval
Xiangnan He, Hanwang Zhang, Min-Yen Kan, and Tat-Seng Chua. 2016 · 2016
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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
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Local collaborative ranking. In Proceedings of the 23rd International Conference on World Wide Web
Joonseok Lee, Samy Bengio, Seungyeon Kim, Guy Lebanon, and Yoram Singer. 2014 · 2014
Cited alongside, same era.
Improving Pairwise Learning for Item Recommendation from Implicit Feedback. In Proceedings of the 7th ACM International Conference on Web Search and Data Mining
Steffen Rendle and Christoph Freudenthaler. 2014 · 2014
Cited alongside, same era.
Trirank: Review-aware explainable recommendation by modeling aspects. In Proceedings of the 24th ACM International on Conference on Information and Knowledge Management
Xiangnan He, Tao Chen, Min-Yen Kan, and Xiao Chen. 2015 · 2015
Cited alongside, same era.
Deep Collaborative Filtering via Marginalized Denoising Auto-encoder. In Proceedings of the 24th ACM International on Conference on Information and Knowledge Management
Sheng Li, Jaya Kawale, and Yun Fu. 2015 · 2015
Cited alongside, same era.
One-Class Collaborative Filtering. In Proceedings of the 8th IEEE International Conference on Data Mining
Rong Pan, Yunhong Zhou, Bin Cao, Nathan N. Liu, Rajan Lukose, Martin Scholz, and Qiang Yang. [n. d.]
Cited in the paper.
Yao Wu, Christopher DuBois, Alice X. Zheng, and Martin Ester. 2016 · 2016
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
Jingyuan Chen, Hanwang Zhang, Xiangnan He, Liqiang Nie, Wei Liu, 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.
Collaborative Metric Learning. In Proceedings of the 26th International Conference on World Wide Web
Cheng-Kang Hsieh, Longqi Yang, Yin Cui, Tsung-Yi Lin, Serge Belongie, and Deborah Estrin. 2017 · 2017
Later among the works it cites.