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Neural Computing and Applications

Erschienen in:

01.11.2016 | Predictive Analytics Using Machine Learning

Two-level matrix factorization for recommender systems

verfasst von: Fangfang Li, Guandong Xu, Longbing Cao

Erschienen in: Neural Computing and Applications | Ausgabe 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.

Vorheriger Artikel Pareto efficient multi-objective optimization for local tuning of analogy-based estimation

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Titel: Two-level matrix factorization for recommender systems
verfasst von: Fangfang Li
Guandong Xu
Longbing Cao
Publikationsdatum: 01.11.2016
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 8/2016
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-015-2060-3

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