UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
Unifying collaborative and content-based filtering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Modeling distances in large-scale networks by matrix factorization
Proceedings of the 4th ACM SIGCOMM conference on Internet measurement
Multi-label informed latent semantic indexing
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Co-clustering by block value decomposition
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Fast maximum margin matrix factorization for collaborative prediction
ICML '05 Proceedings of the 22nd international conference on Machine learning
Orthogonal nonnegative matrix t-factorizations for clustering
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Naïve filterbots for robust cold-start recommendations
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
The Relationships Among Various Nonnegative Matrix Factorization Methods for Clustering
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
A Generalized Divergence Measure for Nonnegative Matrix Factorization
Neural Computation
Combining content and link for classification using matrix factorization
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Predictive discrete latent factor models for large scale dyadic data
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Collaborative Filtering Using Orthogonal Nonnegative Matrix Tri-factorization
ICDMW '07 Proceedings of the Seventh IEEE International Conference on Data Mining Workshops
Non-negative matrix factorization with α-divergence
Pattern Recognition Letters
Relational learning via collective matrix factorization
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Orthogonal Nonnegative Matrix Factorization: Multiplicative Updates on Stiefel Manifolds
IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
Probabilistic matrix tri-factorization
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Weighted nonnegative matrix factorization
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
PAC-Bayesian Analysis of Co-clustering and Beyond
The Journal of Machine Learning Research
Bayesian matrix co-factorization: variational algorithm and Cramér-Rao bound
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
Matrix co-factorization on compressed sensing
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
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
Transfer learning in heterogeneous collaborative filtering domains
Artificial Intelligence
| Hi-index | 0.00 |
Collaborative prediction refers to the task of predicting user preferences on the basis of ratings by other users. Collaborative prediction suffers from the cold start problem where predictions of ratings for new items or predictions of new users' preferences are required. Various methods have been developed to overcome this limitation, exploiting side information such as content information and demographic user data. In this paper we present a matrix factorization method for incorporating side information into collaborative prediction. We develop Weighted Nonnegative Matrix Co-Tri-Factorization (WNMCTF) where we jointly minimize weighted residuals, each of which involves a nonnegative 3-factor decomposition of target or side information matrix. Numerical experiments on MovieLens data confirm the useful behavior of WNMCTF when operating from a cold start.