Factorization Machines

Rendle, Steffen

Factorization Machines

Dateien

Zu diesem Dokument gibt es keine Dateien.

Datum

2010

Publikationstyp

Beitrag zu einem Konferenzband

Publikationsstatus

Published

Erschienen in

2010 IEEE International Conference on Data Mining. IEEE, 2010, pp. 995-1000. ISBN 978-1-4244-9131-5. Available under: doi: 10.1109/ICDM.2010.127

Zusammenfassung

In this paper, we introduce Factorization Machines (FM) which are a new model class that combines the advantages of Support Vector Machines (SVM) with factorization models. Like SVMs, FMs are a general predictor working with any real valued feature vector. In contrast to SVMs, FMs model all interactions between variables using factorized parameters. Thus they are able to estimate interactions even in problems with huge sparsity (like recommender systems) where SVMs fail. We show that the model equation of FMs can be calculated in linear time and thus FMs can be optimized directly. So unlike nonlinear SVMs, a transformation in the dual form is not necessary and the model parameters can be estimated directly without the need of any support vector in the solution. We show the relationship to SVMs and the advantages of FMs for parameter estimation in sparse settings. On the other hand there are many different factorization models like matrix factorization, parallel factor analysis or specialized models like SVD++, PITF or FPMC. The drawback of these models is that they are not applicable for general prediction tasks but work only with special input data. Furthermore their model equations and optimization algorithms are derived individually for each task. We show that FMs can mimic these models just by specifying the input data (i.e. the feature vectors). This makes FMs easily applicable even for users without expert knowledge in factorization models.

Fachgebiet (DDC)

004 Informatik

Schlagwörter

factorization machine, sparse data, tensor factorization, support vector machine

Konferenz

2010 IEEE 10th International Conference on Data Mining (ICDM), 13. Dez. 2010 - 17. Dez. 2010, Sydney, Australia

Zitieren

ISO 690

RENDLE, Steffen, 2010. Factorization Machines. 2010 IEEE 10th International Conference on Data Mining (ICDM). Sydney, Australia, 13. Dez. 2010 - 17. Dez. 2010. In: 2010 IEEE International Conference on Data Mining. IEEE, 2010, pp. 995-1000. ISBN 978-1-4244-9131-5. Available under: doi: 10.1109/ICDM.2010.127

BibTex

@inproceedings{Rendle2010-12Facto-12699,
  year={2010},
  doi={10.1109/ICDM.2010.127},
  title={Factorization Machines},
  isbn={978-1-4244-9131-5},
  publisher={IEEE},
  booktitle={2010 IEEE International Conference on Data Mining},
  pages={995--1000},
  author={Rendle, Steffen}
}

RDF

<rdf:RDF
    xmlns:dcterms="http://purl.org/dc/terms/"
    xmlns:dc="http://purl.org/dc/elements/1.1/"
    xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
    xmlns:bibo="http://purl.org/ontology/bibo/"
    xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#"
    xmlns:foaf="http://xmlns.com/foaf/0.1/"
    xmlns:void="http://rdfs.org/ns/void#"
    xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > 
  <rdf:Description rdf:about="https://kops.uni-konstanz.de/server/rdf/resource/123456789/12699">
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2011-09-08T06:32:24Z</dc:date>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dc:rights>terms-of-use</dc:rights>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2011-09-08T06:32:24Z</dcterms:available>
    <dcterms:bibliographicCitation>Publ. in: 2010 IEEE 10th International Conference on Data Mining (ICDM 2010) : Sydney, Australia, 13 - 17 December 2010 ; [proceedings] / [IEEE Computer Society]. Ed.: Geoffrey I. Webb ... . Piscataway, NJ : IEEE, 2010, pp. 995-1000</dcterms:bibliographicCitation>
    <dcterms:issued>2010-12</dcterms:issued>
    <dcterms:abstract xml:lang="eng">In this paper, we introduce Factorization Machines (FM) which are a new model class that combines the advantages of Support Vector Machines (SVM) with factorization models. Like SVMs, FMs are a general predictor working with any real valued feature vector. In contrast to SVMs, FMs model all interactions between variables using factorized parameters. Thus they are able to estimate interactions even in problems with huge sparsity (like recommender systems) where SVMs fail. We show that the model equation of FMs can be calculated in linear time and thus FMs can be optimized directly. So unlike nonlinear SVMs, a transformation in the dual form is not necessary and the model parameters can be estimated directly without the need of any support vector in the solution. We show the relationship to SVMs and the advantages of FMs for parameter estimation in sparse settings. On the other hand there are many different factorization models like matrix factorization, parallel factor analysis or specialized models like SVD++, PITF or FPMC. The drawback of these models is that they are not applicable for general prediction tasks but work only with special input data. Furthermore their model equations and optimization algorithms are derived individually for each task. We show that FMs can mimic these models just by specifying the input data (i.e. the feature vectors). This makes FMs easily applicable even for users without expert knowledge in factorization models.</dcterms:abstract>
    <dc:language>eng</dc:language>
    <bibo:uri rdf:resource="http://kops.uni-konstanz.de/handle/123456789/12699"/>
    <dc:contributor>Rendle, Steffen</dc:contributor>
    <dcterms:rights rdf:resource="https://rightsstatements.org/page/InC/1.0/"/>
    <dc:creator>Rendle, Steffen</dc:creator>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dcterms:title>Factorization Machines</dcterms:title>
  </rdf:Description>
</rdf:RDF>

Universitätsbibliographie

Nein