An accelerated gradient method for trace norm minimization
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
A spatio-temporal approach to collaborative filtering
Proceedings of the third ACM conference on Recommender systems
When Is There a Representer Theorem? Vector Versus Matrix Regularizers
The Journal of Machine Learning Research
The Journal of Machine Learning Research
Learning incoherent sparse and low-rank patterns from multiple tasks
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Spectral Regularization Algorithms for Learning Large Incomplete Matrices
The Journal of Machine Learning Research
Online evolutionary collaborative filtering
Proceedings of the fourth ACM conference on Recommender systems
Multiple domain user personalization
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning a Robust Relevance Model for Search Using Kernel Methods
The Journal of Machine Learning Research
Learning multiple models for exploiting predictive heterogeneity in recommender systems
Proceedings of the 2nd International Workshop on Information Heterogeneity and Fusion in Recommender Systems
Generalizing matrix factorization through flexible regression priors
Proceedings of the fifth ACM conference on Recommender systems
Trace Norm Regularization: Reformulations, Algorithms, and Multi-Task Learning
SIAM Journal on Optimization
Learning Incoherent Sparse and Low-Rank Patterns from Multiple Tasks
ACM Transactions on Knowledge Discovery from Data (TKDD)
Optimization with Sparsity-Inducing Penalties
Foundations and Trends® in Machine Learning
Transfer learning to predict missing ratings via heterogeneous user feedbacks
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Kernelization of matrix updates, when and how?
ALT'12 Proceedings of the 23rd international conference on Algorithmic Learning Theory
Transfer learning in heterogeneous collaborative filtering domains
Artificial Intelligence
Pairwise support vector machines and their application to large scale problems
The Journal of Machine Learning Research
Learning output kernels for multi-task problems
Neurocomputing
Retargeted matrix factorization for collaborative filtering
Proceedings of the 7th ACM conference on Recommender systems
Learning bilinear model for matching queries and documents
The Journal of Machine Learning Research
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We present a general approach for collaborative filtering (CF) using spectral regularization to learn linear operators mapping a set of "users" to a set of possibly desired "objects". In particular, several recent low-rank type matrix-completion methods for CF are shown to be special cases of our proposed framework. Unlike existing regularization-based CF, our approach can be used to incorporate additional information such as attributes of the users/objects---a feature currently lacking in existing regularization-based CF approaches---using popular and well-known kernel methods. We provide novel representer theorems that we use to develop new estimation methods. We then provide learning algorithms based on low-rank decompositions and test them on a standard CF data set. The experiments indicate the advantages of generalizing the existing regularization-based CF methods to incorporate related information about users and objects. Finally, we show that certain multi-task learning methods can be also seen as special cases of our proposed approach.