| Autor: |
SZWABE, ANDRZEJ, MISIOREK, PAWEŁ, CIESIELCZYK, MICHAŁ |
| Předmět: |
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| Zdroj: |
Schedae Informaticae; 2015, Vol. 24, p103-112, 10p |
| Abstrakt: |
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. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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