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Recommender systems have been studied extensively due to their practical use in many real-world scenarios.
Nonlinear principal component analysis using autoassociative neural networks
Mark A Kramer. 1991 · 1991
Earlier work this paper cites.
A generalized mean field algorithm for variational inference in exponential families. In UAI
Eric P Xing, Michael I Jordan, and Stuart Russell. 2002 · 2002
Earlier work this paper cites.
SLIM: Sparse Linear Methods for Top-N Recommender Systems. In ICDM
Xia Ning and George Karypis. 2011 · 2011
Earlier work this paper cites.
Sparse linear methods with side information for top-n recommendations. In RecSys
Xia Ning and George Karypis. 2012 · 2012
Earlier work this paper cites.
Variational Bayesian Inference with Stochastic Search. In ICML
John William Paisley, David M. Blei, and Michael I. Jordan. 2012 · 2012
Earlier work this paper cites.
FISM: factored item similarity models for top-N recommender systems. In SIGKDD
Santosh Kabbur, Xia Ning, and George Karypis. 2013 · 2013
Earlier work this paper cites.
Auto-Encoding Variational Bayes
Diederik P. Kingma and Max Welling. 2013 · 2013
Earlier work this paper cites.
Hidden factors and hidden topics: understanding rating dimensions with review text. In RecSys
Julian J. McAuley and Jure Leskovec. 2013 · 2013
Earlier work this paper cites.
User-Specific Feature-Based Similarity Models for Top- n Recommendation of New Items
Asmaa Elbadrawy and George Karypis. 2015 · 2015
Earlier work this paper cites.
Deep Collaborative Filtering via Marginalized Denoising Auto-encoder. In CIKM
Sheng Li, Jaya Kawale, and Yun Fu. 2015 · 2015
Cited alongside, same era.
AutoRec: Autoencoders Meet Collaborative Filtering. In WWW
Suvash Sedhain, Aditya Krishna Menon, Scott Sanner, and Lexing Xie. 2015 · 2015
Cited alongside, same era.
Feature-based factorized Bilinear Similarity Model for Cold-Start Top- n Item Recommendation. In SDM
Mohit Sharma, Jiayu Zhou, Junling Hu, and George Karypis. 2015 · 2015
Cited alongside, same era.
Collaborative Deep Learning for Recommender Systems. In SIGKDD
Hao Wang, Naiyan Wang, and Dit-Yan Yeung. 2015 · 2015
Cited alongside, same era.
Hybrid Recommender System based on Autoencoders. In DLRS
Florian Strub, Romaric Gaudel, and Jérémie Mary. 2016 · 2016
Cited alongside, same era.
Collaborative Denoising Auto-Encoders for Top-N Recommender Systems. In WSDM
Top-N Recommendation with High-Dimensional Side Information via Locality Preserving Projection. In SIGIR
Yifan Chen, Xiang Zhao, and Maarten de Rijke. 2017 · 2017
Later among the works it cites.
Neural Collaborative Filtering. In WWW
Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017 · 2017
Later among the works it cites.
Augmented Variational Autoencoders for Collaborative Filtering with Auxiliary Information. In CIKM
Wonsung Lee, Kyungwoo Song, and Il-Chul Moon. 2017 · 2017
Later among the works it cites.
Collaborative Variational Autoencoder for Recommender Systems. In SIGKDD
Xiaopeng Li and James She. 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. 2017 · 2017
Later among the works it cites.
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Yao Wu, Christopher DuBois, Alice X. Zheng, and Martin Ester. 2016 · 2016
Cited alongside, same era.
Predictive Collaborative Filtering with Side Information. In IJCAI
Feipeng Zhao, Min Xiao, and Yuhong Guo. 2016 · 2016
Cited alongside, same era.
A Neural Autoregressive Approach to Collaborative Filtering. In ICML
Yin Zheng, Bangsheng Tang, Wenkui Ding, and Hanning Zhou. 2016 · 2016
Cited alongside, same era.
Learning Discriminative Recommendation Systems with Side Information. In IJCAI
Feipeng Zhao and Yuhong Guo. 2017 · 2017
Later among the works it cites.
Representation learning via Dual-Autoencoder for recommendation
Fuzhen Zhuang, Zhiqiang Zhang, Mingda Qian, Chuan Shi, Xing Xie, and Qing He. 2017 · 2017
Later among the works it cites.
Variational Autoencoders for Collaborative Filtering. In WWW
Dawen Liang, Rahul G. Krishnan, Matthew D. Hoffman, and Tony Jebara. 2018 · 2018
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