A Generalized Representer Theorem
COLT '01/EuroCOLT '01 Proceedings of the 14th Annual Conference on Computational Learning Theory and and 5th European Conference on Computational Learning Theory
IEEE Transactions on Knowledge and Data Engineering
Learning the Kernel Function via Regularization
The Journal of Machine Learning Research
Fast maximum margin matrix factorization for collaborative prediction
ICML '05 Proceedings of the 22nd international conference on Machine learning
Computing and applying trust in web-based social networks
Computing and applying trust in web-based social networks
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
A framework for simultaneous co-clustering and learning from complex data
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
A tutorial on spectral clustering
Statistics and Computing
SoRec: social recommendation using probabilistic matrix factorization
Proceedings of the 17th ACM conference on Information and knowledge management
TrustWalker: a random walk model for combining trust-based and item-based recommendation
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
A matrix factorization technique with trust propagation for recommendation in social networks
Proceedings of the fourth ACM conference on Recommender systems
Comparison of implicit and explicit feedback from an online music recommendation service
Proceedings of the 1st International Workshop on Information Heterogeneity and Fusion in Recommender Systems
Retargeted matrix factorization for collaborative filtering
Proceedings of the 7th ACM conference on Recommender systems
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Pairwise interaction networks capture inter-user dependencies (e.g. social networks) and inter-item dependencies (e.g item categories) that provide insight into user and item behavior. It is often assumed that such interaction information is informative for preference prediction. This may not be the case, as the some of the observed interactions may not be correlated with the preferences, and their use may negatively impact performance by introducing undesired noise. We propose an approach for weighting each interaction, such that we can determine the importance of each interaction to the preference prediction task. We model the preferences using kernel matrix factorization; where the kernels capture the weighted effects of the interactions. Our approach is validated on Last.fm and Movielens datasets; which include multiple sources of explicit and implicit inter-user and inter-item interactions. Our experiments suggest that learning the most important interactions can improve recommendation performance when compared to the standard matrix factorization approach.