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Multilinear Filtering Based on a Hierarchical Structure of Covariance Matrices

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Szwabe A., Ciesielczyk M., Misiorek P.

Schedae Informaticae

2015

vol. 24

We propose a novel model of multilinear filtering based on a hierarchical structure of covariance matrices – each matrix being extracted from the input tensor in accordance to a specific set-theoretic model of data generalization, such as derivation of expectation values. The experimental analysis results presented in this paper confirm that the investigated approaches to tensor-based data representation and processing outperform the standard collaborative filtering approach in the ‘cold-start’ personalized recommendation scenario (of very sparse input data). Furthermore, it has been shown that the proposed method is superior to standard tensor-based frameworks such as N-way Random Indexing (NRI) and Higher-Order Singular Value Decomposition (HOSVD) in terms of both the AUROC measure and computation time.

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1 Schedae Informaticae

1 2015

1 Ciesielczyk M.

1 Misiorek P.

1 Szwabe A.

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Multilinear Filtering Based on a Hierarchical Structure of Covariance Matrices