Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
Fast maximum margin matrix factorization for collaborative prediction
ICML '05 Proceedings of the 22nd international conference on Machine learning
Collaborative prediction using ensembles of Maximum Margin Matrix Factorizations
ICML '06 Proceedings of the 23rd international conference on Machine learning
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Online-updating regularized kernel matrix factorization models for large-scale recommender systems
Proceedings of the 2008 ACM conference on Recommender systems
Collaborative filtering with temporal dynamics
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Investigation of various matrix factorization methods for large recommender systems
Proceedings of the 2nd KDD Workshop on Large-Scale Recommender Systems and the Netflix Prize Competition
A survey of collaborative filtering techniques
Advances in Artificial Intelligence
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Matrix factorization (MF) is one of the well-known methods in collaborative filtering to build accurate and efficient recommender systems. While in all the previous studies about MF items are considered to be of the same type, in some applications, items are divided into different groups, related to each other in a defined hierarchy (e.g. artists, albums and tracks). This paper proposes Hierarchical Matrix Factorization (HMF), a method that incorporates such relations into MF, to model the item vectors. This method is applicable in the situations that item groups form a general-to-specific hierarchy with child-to-parent (many-to-one or many-to-many) relationship between successive layers. This study evaluates the accuracy of the proposed method in comparison to basic MF on the Yahoo! Music dataset by examining three different hierarchical models. The results in all the cases demonstrate the superiority of HMF. In addition to the effectiveness of HMF in improving the prediction accuracy in the mentioned scenarios, this model is very efficient and scalable. Furthermore, it can be readily integrated with the other variations of MF.