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Recommender systems often use latent features to explain the behaviors of users and capture the properties of items.
Spectra of some self-exciting and mutually exciting point processes
Alan G Hawkes. 1971 · 1971
Earlier work this paper cites.
Point processes
D.R. Cox and V. Isham. 1980 · 1980
Earlier work this paper cites.
Multivariate point processes
D.R. Cox and P.A.W. Lewis. 2006 · 2006
Earlier work this paper cites.
An introduction to the theory of point processes: volume II: general theory and structure
D.J. Daley and D. Vere-Jones. 2007 · 2007
Earlier work this paper cites.
Survival and event history analysis: a process point of view
Odd Aalen, Ornulf Borgan, and Hakon Gjessing. 2008 · 2008
Earlier work this paper cites.
Bayesian probabilistic matrix factorization using Markov chain Monte Carlo. In ICML
R. Salakhutdinov and A. Mnih. 2008 · 2008
Earlier work this paper cites.
Regression-based latent factor models. In KDD
D. Agarwal and B.-C. Chen. 2009 · 2009
Earlier work this paper cites.
Large-scale behavioral targeting. In KDD
Y. Chen, D. Pavlov, and J.F. Canny. 2009 · 2009
Earlier work this paper cites.
Collaborative filtering with temporal dynamics. In KDD
Y. Koren. 2009 · 2009
Earlier work this paper cites.
Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering. In Recsys
Alexandros Karatzoglou, Xavier Amatriain, Linas Baltrunas, and Nuria Oliver. 2010 · 2010
Earlier work this paper cites.
Temporal Collaborative Filtering with Bayesian Probabilistic Tensor Factorization.. In SDM
Liang Xiong, Xi Chen, Tzu-Kuo Huang, Jeff G. Schneider, and Jaime G. Carbonell. 2010 · 2010
Earlier work this paper cites.
Collaborative filtering recommender systems
Michael D Ekstrand, John T Riedl, and Joseph A Konstan. 2011 · 2011
Earlier work this paper cites.
OrdRec: an ordinal model for predicting personalized item rating distributions. In RecSys
Yehuda Koren and Joe Sill. 2011 · 2011
Earlier work this paper cites.
Like like alike: joint friendship and interest propagation in social networks. In WWW
Shuang-Hong Yang, Bo Long, Alex Smola, Narayanan Sadagopan, Zhaohui Zheng, and Hongyuan Zha. 2011 · 2011
Cited alongside, same era.
On tensors, sparsity, and nonnegative factorizations
Eric C Chi and Tamara G Kolda. 2012 · 2012
Cited alongside, same era.
Noise-contrastive estimation of unnormalized statistical models, with applications to natural image statistics
Michael U Gutmann and Aapo Hyvärinen. 2012 · 2012
Cited alongside, same era.
General Functional Matrix Factorization using Gradient Boosting. In Proceeding of 30th International Conference on Machine Learning (ICML’13)
Tianqi Chen, Hang Li, Qiang Yang, and Yong Yu. 2013 · 2013
Cited alongside, same era.
Learning word embeddings efficiently with noise-contrastive estimation. In Advances in Neural Information Processing Systems
Andriy Mnih and Koray Kavukcuoglu. 2013 · 2013
Cited alongside, same era.
General factorization framework for context-aware recommendations
Balázs Hidasi and Domonkos Tikk. 2015 · 2015
Later among the works it cites.
Just in Time Recommendations: Modeling the Dynamics of Boredom in Activity Streams. In WSDM
Komal Kapoor, Karthik Subbian, Jaideep Srivastava, and Paul Schrater. 2015 · 2015
Later among the works it cites.
Who, What, When, and Where: Multi-Dimensional Collaborative Recommendations Using Tensor Factorization on Sparse User-Generated Data. In WWW
Jiayu Zhou Juhan Lee Preeti Bhargava, Thomas Phan. 2015 · 2015
Later among the works it cites.
Collaborative Deep Learning for Recommender Systems. In KDD
Hao Wang, Naiyan Wang, and Dit-Yan Yeung. 2015 · 2015
Later among the works it cites.
Rubik: Knowledge Guided Tensor Factorization and Completion for Health Data Analytics. In KDD
Yichen Wang, Robert Chen, Joydeep Ghosh, Joshua C Denny, Abel Kho, You Chen, Bradley A Malin, and Jimeng Sun. 2015 · 2015
Later among the works it cites.
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A collaborative kalman filter for time-evolving dyadic processes. In ICDM
San Gultekin and John Paisley. 2014 · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba. 2014 · 2014
Cited alongside, same era.
Beyond Clicks: Dwell Time for Personalization. In RecSys
Xing Yi, Liangjie Hong, Erheng Zhong, Nanthan Nan Liu, and Suju Rajan. 2014 · 2014
Cited alongside, same era.
Diamond sampling for approximate maximum all-pairs dot-product (MAD) search. In Data Mining (ICDM), 2015 IEEE International Conference on
Grey Ballard, Tamara G Kolda, Ali Pinar, and C Seshadhri. 2015 · 2015
Cited alongside, same era.
Dynamic Poisson Factorization. In RecSys
Laurent Charlin, Rajesh Ranganath, James McInerney, and David M Blei. 2015 · 2015
Cited alongside, same era.
Time Sensitive Recommendation From Recurrent User Activities. In NIPS
Nan Du, Yichen Wang, Niao He, and Le Song. 2015 · 2015
Cited alongside, same era.
Scalable Recommendation with Hierarchical Poisson Factorization
Prem Gopalan, Jake M Hofman, and David M Blei. 2015 · 2015
Cited alongside, same era.
Detecting Emotions in Social Media: A Constrained Optimization Approach. In IJCAI
Yichen Wang and Aditya Pal. 2015 · 2015
Later among the works it cites.
Discriminative Embeddings of Latent Variable Models for Structured Data. In ICML
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Closest in time.
Recurrent Marked Temporal Point Processes: Embedding Event History to Vector. In KDD
Nan Du, Hanjun Dai, Rakshit Trivedi, Utkarsh Upadhyay, Manuel Gomez-Rodriguez, and Le Song. 2016 · 2016
Closest in time.
Session-based Recommendations With Recurrent Neural Networks. In ICLR
Balazs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2016 · 2016
Closest in time.
Collaborative Recurrent Neural Networks for Dynamic Recommender Systems
Young-Jun Ko, Lucas Maystre, and Matthias Grossglauser. 2016 · 2016
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Multi-Rate Deep Learning for Temporal Recommendation. In Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval
Yang Song, Ali Mamdouh Elkahky, and Xiaodong He. 2016 · 2016
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Improved Recurrent Neural Networks for Session-based Recommendations
Yong K Tan, Xinxing Xu, and Yong Liu. 2016 · 2016
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