GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
A view of the EM algorithm that justifies incremental, sparse, and other variants
Learning in graphical models
Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
A Framework for Collaborative, Content-Based and Demographic Filtering
Artificial Intelligence Review - Special issue on data mining on the Internet
Machine Learning
A general maximum likelihood analysis of measurement error in generalized linear models
Statistics and Computing
Information-theoretic co-clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Fully automatic cross-associations
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Biclustering Algorithms for Biological Data Analysis: A Survey
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Unsupervised learning on k-partite graphs
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Clustering with Bregman Divergences
The Journal of Machine Learning Research
Convergence Theorems for Generalized Alternating Minimization Procedures
The Journal of Machine Learning Research
A Generalized Maximum Entropy Approach to Bregman Co-clustering and Matrix Approximation
The Journal of Machine Learning Research
Distributed learning using generative models
Distributed learning using generative models
Approximation algorithms for co-clustering
Proceedings of the twenty-seventh ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Relational learning via collective matrix factorization
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Regression-based latent factor models
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining for the most certain predictions from dyadic data
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
A spatio-temporal approach to collaborative filtering
Proceedings of the third ACM conference on Recommender systems
Pairwise preference regression for cold-start recommendation
Proceedings of the third ACM conference on Recommender systems
A unified approach to building hybrid recommender systems
Proceedings of the third ACM conference on Recommender systems
Weighted Nonnegative Matrix Co-Tri-Factorization for Collaborative Prediction
ACML '09 Proceedings of the 1st Asian Conference on Machine Learning: Advances in Machine Learning
Distributed nonnegative matrix factorization for web-scale dyadic data analysis on mapreduce
Proceedings of the 19th international conference on World wide web
I/O scalable Bregman co-clustering
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
SCOAL: A framework for simultaneous co-clustering and learning from complex data
ACM Transactions on Knowledge Discovery from Data (TKDD)
Smart news feeds for social networks using scalable joint latent factor models
Proceedings of the 20th international conference companion on World wide web
Scalable distributed inference of dynamic user interests for behavioral targeting
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Co-clustering with augmented data matrix
DaWaK'11 Proceedings of the 13th international conference on Data warehousing and knowledge discovery
Learning multiple models for exploiting predictive heterogeneity in recommender systems
Proceedings of the 2nd International Workshop on Information Heterogeneity and Fusion in Recommender Systems
Gateway finder in large graphs: problem definitions and fast solutions
Information Retrieval
Latent factor blockmodel for modelling relational data
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
Co-clustering with augmented matrix
Applied Intelligence
CoBaFi: collaborative bayesian filtering
Proceedings of the 23rd international conference on World wide web
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We propose a novel statistical method to predict large scale dyadic response variables in the presence of covariate information. Our approach simultaneously incorporates the effect of covariates and estimates local structure that is induced by interactions among the dyads through a discrete latent factor model. The discovered latent factors provide a redictive model that is both accurate and interpretable. We illustrate our method by working in a framework of generalized linear models, which include commonly used regression techniques like linear regression, logistic regression and Poisson regression as special cases. We also provide scalable generalized EM-based algorithms for model fitting using both "hard" and "soft" cluster assignments. We demonstrate the generality and efficacy of our approach through large scale simulation studies and analysis of datasets obtained from certain real-world movie recommendation and internet advertising applications.