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Neural Computing and Applications
Published in: Neural Computing and Applications 8/2016

01-11-2016 | Predictive Analytics Using Machine Learning

Two-level matrix factorization for recommender systems

Authors: Fangfang Li, Guandong Xu, Longbing Cao

Published in: Neural Computing and Applications | Issue 8/2016

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Abstract

Many existing recommendation methods such as matrix factorization (MF) mainly rely on user–item rating matrix, which sometimes is not informative enough, often suffering from the cold-start problem. To solve this challenge, complementary textual relations between items are incorporated into recommender systems (RS) in this paper. Specifically, we first apply a novel weighted textual matrix factorization (WTMF) approach to compute the semantic similarities between items, then integrate the inferred item semantic relations into MF and propose a two-level matrix factorization (TLMF) model for RS. Experimental results on two open data sets not only demonstrate the superiority of TLMF model over benchmark methods, but also show the effectiveness of TLMF for solving the cold-start problem.
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Footnotes
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Metadata
Title
Two-level matrix factorization for recommender systems
Authors
Fangfang Li
Guandong Xu
Longbing Cao
Publication date
01-11-2016
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 8/2016
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-015-2060-3

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