Mixture Matrix Approximation for Collaborative Filtering

[1]  Gediminas Adomavicius,et al.  Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions , 2005, IEEE Transactions on Knowledge and Data Engineering.

[2]  Yifan Hu,et al.  Collaborative Filtering for Implicit Feedback Datasets , 2008, 2008 Eighth IEEE International Conference on Data Mining.

[3]  Lars Schmidt-Thieme,et al.  Online-updating regularized kernel matrix factorization models for large-scale recommender systems , 2008, RecSys '08.

[4]  Sonia A. Bhaskar,et al.  Probabilistic Low-Rank Matrix Completion from Quantized Measurements , 2016, J. Mach. Learn. Res..

[5]  Dirk Van,et al.  Ensemble Methods: Foundations and Algorithms , 2012 .

[6]  Lars Schmidt-Thieme,et al.  BPR: Bayesian Personalized Ranking from Implicit Feedback , 2009, UAI.

[7]  Steffen Rendle,et al.  Improving pairwise learning for item recommendation from implicit feedback , 2014, WSDM.

[8]  Yang Cao,et al.  Categorical matrix completion , 2015, 2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP).

[9]  Nuria Oliver,et al.  I Like It... I Like It Not: Evaluating User Ratings Noise in Recommender Systems , 2009, UMAP.

[10]  Greg Linden,et al.  Amazon . com Recommendations Item-to-Item Collaborative Filtering , 2001 .

[11]  Patrick Seemann,et al.  Matrix Factorization Techniques for Recommender Systems , 2014 .

[12]  Yoram Singer,et al.  Local Low-Rank Matrix Approximation , 2013, ICML.

[13]  B. Frey,et al.  Probabilistic Sparse Matrix Factorization , 2004 .

[14]  Li Shang,et al.  AdaError: An Adaptive Learning Rate Method for Matrix Approximation-based Collaborative Filtering , 2018, WWW.

[15]  George Karypis,et al.  SLIM: Sparse Linear Methods for Top-N Recommender Systems , 2011, 2011 IEEE 11th International Conference on Data Mining.

[16]  Alexander J. Smola,et al.  ACCAMS: Additive Co-Clustering to Approximate Matrices Succinctly , 2014, WWW.

[17]  Ruslan Salakhutdinov,et al.  Bayesian probabilistic matrix factorization using Markov chain Monte Carlo , 2008, ICML '08.

[18]  Yoshua Bengio,et al.  Deep Sparse Rectifier Neural Networks , 2011, AISTATS.

[19]  A BhaskarSonia Probabilistic low-rank matrix completion from quantized measurements , 2016 .

[20]  Xi Zhang,et al.  Recommendation by Mining Multiple User Behaviors with Group Sparsity , 2014, AAAI.

[21]  Tara N. Sainath,et al.  Improving deep neural networks for LVCSR using rectified linear units and dropout , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[22]  Anders Krogh,et al.  Neural Network Ensembles, Cross Validation, and Active Learning , 1994, NIPS.

[23]  Li Shang,et al.  WEMAREC: Accurate and Scalable Recommendation through Weighted and Ensemble Matrix Approximation , 2015, SIGIR.

[24]  Tat-Seng Chua,et al.  Neural Collaborative Filtering , 2017, WWW.

[25]  Michael Rabadi,et al.  Kernel Methods for Machine Learning , 2015 .

[26]  Alexander J. Smola,et al.  Maximum Margin Matrix Factorization for Collaborative Ranking , 2007 .

[27]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[28]  Panagiotis Symeonidis,et al.  Matrix and Tensor Factorization Techniques for Recommender Systems , 2017, SpringerBriefs in Computer Science.

[29]  John R. Anderson,et al.  Beyond Globally Optimal: Focused Learning for Improved Recommendations , 2017, WWW.

[30]  Edward Raff,et al.  JSAT: Java Statistical Analysis Tool, a Library for Machine Learning , 2017, J. Mach. Learn. Res..

[31]  Li Shang,et al.  Low-Rank Matrix Approximation with Stability , 2016, ICML.

[32]  John Riedl,et al.  Application of Dimensionality Reduction in Recommender System - A Case Study , 2000 .

[33]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[34]  Ameet Talwalkar,et al.  Divide-and-Conquer Matrix Factorization , 2011, NIPS.

[35]  Wei Liu,et al.  Mixture-Rank Matrix Approximation for Collaborative Filtering , 2017, NIPS.

[36]  Ruslan Salakhutdinov,et al.  Probabilistic Matrix Factorization , 2007, NIPS.

[37]  George Karypis,et al.  FISM: factored item similarity models for top-N recommender systems , 2013, KDD.

[38]  Qiang Yang,et al.  One-Class Collaborative Filtering , 2008, 2008 Eighth IEEE International Conference on Data Mining.

[39]  Abhinandan Das,et al.  Google news personalization: scalable online collaborative filtering , 2007, WWW '07.

[40]  Steven C. H. Hoi,et al.  Cost-Sensitive Online Classification , 2012, 2012 IEEE 12th International Conference on Data Mining.

[41]  Qiang Wu,et al.  McRank: Learning to Rank Using Multiple Classification and Gradient Boosting , 2007, NIPS.

[42]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[43]  Shi Feng,et al.  Localized matrix factorization for recommendation based on matrix block diagonal forms , 2013, WWW.

[44]  Neil J. Hurley,et al.  How Diverse Is Your Audience? Exploring Consumer Diversity in Recommender Systems , 2017, RecSys Posters.

[45]  Arkadiusz Paterek,et al.  Improving regularized singular value decomposition for collaborative filtering , 2007 .

[46]  Samy Bengio,et al.  Local collaborative ranking , 2014, WWW.

[47]  Li Shang,et al.  ERMMA: Expected Risk Minimization for Matrix Approximation-based Recommender Systems , 2017, AAAI.

[48]  D. Böhning Multinomial logistic regression algorithm , 1992 .

[49]  Yehuda Koren,et al.  Matrix Factorization Techniques for Recommender Systems , 2009, Computer.

[50]  Michael J. Pazzani,et al.  Learning Collaborative Information Filters , 1998, ICML.

[51]  Yehuda Koren,et al.  Factorization meets the neighborhood: a multifaceted collaborative filtering model , 2008, KDD.

[52]  Li Shang,et al.  MPMA: Mixture Probabilistic Matrix Approximation for Collaborative Filtering , 2016, IJCAI.

[53]  Tat-Seng Chua,et al.  Fast Matrix Factorization for Online Recommendation with Implicit Feedback , 2016, SIGIR.