Fetching the paper…
Reading the bibliography…
Modeling user-item interaction patterns is an important task for personalized recommendations.
Amazon.com recommendations: item-to-item collaborative filtering
G. Linden, B. Smith, and J. York · 2003
Earlier work this paper cites.
Probabilistic matrix factorization
Ruslan Salakhutdinov and Andriy Mnih · 2007
Earlier work this paper cites.
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Yehuda Koren · 2008
Earlier work this paper cites.
Matrix factorization techniques for recommender systems
Yehuda Koren, Robert Bell, and Chris Volinsky · 2009
Earlier work this paper cites.
Learning to rank for information retrieval
Tie-Yan Liu et al · 2009
Earlier work this paper cites.
Bpr: Bayesian personalized ranking from implicit feedback
Steffen Rendle, Christoph Freudenthaler, et al · 2009
Earlier work this paper cites.
Slim: Sparse linear methods for top-n recommender systems
X. Ning and G. Karypis · 2011
Earlier work this paper cites.
Evaluating recommendation systems
Guy Shani and Asela Gunawardana · 2011
Earlier work this paper cites.
A novel bayesian similarity measure for recommender systems
G. Guo, J. Zhang, and N. Yorke-Smith · 2013
Earlier work this paper cites.
Deep content-based music recommendation
Aaron Van den Oord, Sander Dieleman, and Benjamin Schrauwen · 2013
Earlier work this paper cites.
Optimizing top-n collaborative filtering via dynamic negative item sampling
Weinan Zhang, Tianqi Chen, Jun Wang, and Yong Yu · 2013
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 2014
Cited alongside, same era.
Dropout: A simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, et al · 2014
Cited alongside, same era.
Frappe: Understanding the usage and perception of mobile app recommendations in-the-wild
Linas Baltrunas, Karen Church, et al · 2015
Cited alongside, same era.
The movielens datasets: History and context
F. Maxwell Harper and Joseph A. Konstan · 2015
Cited alongside, same era.
Session-based recommendations with recurrent neural networks
Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk · 2015
Cited alongside, same era.
Convolutional matrix factorization for document context-aware recommendation
Donghyun Kim, Chanyoung Park, et al · 2016
Later among the works it cites.
Neural factorization machines for sparse predictive analytics
Xiangnan He and Tat-Seng Chua · 2017
Later among the works it cites.
Neural collaborative filtering
Xiangnan He, Lizi Liao, et al · 2017
Later among the works it cites.
Deep learning for recommender systems
Alexandros Karatzoglou and Balázs Hidasi · 2017
Later among the works it cites.
Recurrent recommender networks
Chao-Yuan Wu, Amr Ahmed, et al · 2017
Later among the works it cites.
Deep matrix factorization models for recommender systems
HongJian Xue, Xinyu Dai, et al · 2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Advances in collaborative filtering
Yehuda Koren and Robert Bell · 2015
Cited alongside, same era.
Recommender systems handbook
Francesco Ricci, Lior Rokach, Bracha Shapira, and Paul B Kantor · 2015
Cited alongside, same era.
Autorec: Autoencoders meet collaborative filtering
Suvash Sedhain, Aditya Krishna Menon, et al · 2015
Cited alongside, same era.
Collaborative deep learning for recommender systems
Hao Wang, Naiyan Wang, and Dit-Yan Yeung · 2015
Cited alongside, same era.
Wide & deep learning for recommender systems
Heng-Tze Cheng, Levent Koc, et al · 2016
Cited alongside, same era.
Vbpr: Visual bayesian personalized ranking from implicit feedback
Ruining He and Julian McAuley · 2016
Cited alongside, same era.
Shuai Zhang, Lina Yao, and Aixin Sun · 2017
Later among the works it cites.
Autosvd++: An efficient hybrid collaborative filtering model via contractive auto-encoders
Shuai Zhang, Lina Yao, and Xiwei Xu · 2017
Later among the works it cites.
Joint deep modeling of users and items using reviews for recommendation
Lei Zheng, Vahid Noroozi, et al · 2017
Later among the works it cites.
Latent relational metric learning via memory-based attention for collaborative ranking
Yi Tay, Luu Anh Tuan, et al · 2018
Closest in time.
Multi-pointer co-attention networks for recommendation
Yi Tay, Luu Anh Tuan, et al · 2018
Closest in time.