Latent association rule cluster based model to extract topics for classification and recommendation applications

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dc.contributor.authorSantos, Fabiano Fernandes dos
dc.contributor.authorDomingues, Marcos Aurelio
dc.contributor.authorSundermann, Camila Vaccari
dc.contributor.authorCarvalho, Veronica Oliveira de [UNESP]
dc.contributor.authorMoura, Maria Fernanda
dc.contributor.authorRezende, Solange Oliveira
dc.contributor.institutionUniversidade de São Paulo (USP)
dc.contributor.institutionUniversidade Estadual de Maringá (UEM)
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionEmpresa Brasileira de Pesquisa Agropecuária (EMBRAPA)
dc.date.accessioned2019-10-04T13:28:17Z
dc.date.available2019-10-04T13:28:17Z
dc.date.issued2018-12-01
dc.description.abstractThe quality of any text mining technique is highly dependent on the features that are used to represent the document collection. A classical form of document representation is the vector space model (VSM), according to which the documents are represented as vectors of weights that correspond to the features of the documents. The bag-of-words model is the most popular VSM approach due to its simplicity and general applicability, but this model does not include term dependency and has a high dimensionality. In the literature, several models for document representation have been proposed in order to capture the dependency of terms. Among them, the topic model representation is one of the most interesting approaches - since it describes the collection of documents in a way that reveals their internal structure and the interrelationships therein, and also provides a dimensionality reduction. However, even for topic models, the efficient extraction of information concerning the relations among terms for document representation is still a major research challenge. In order to address this issue, we proposed thelatent association rule cluster based model (LARCM). The LARCM is a non-probabilistic topic model that makes use of association rule clustering to build a document representation with low dimensionality in such a way that each feature (i.e., topic) is comprised of information concerning relations among the terms. We evaluated the interpretability of the topics obtained by using our proposed model against the ones provided by the traditional latent dirichlet allocation (LDA) model and the LDA model using a document representation that includes correlated terms (i.e., bag-of-related-words). The experimental results indicated that the LARCM provides topics with better interpretability than the LDA models. Additionally, we used the topics obtained by the LARCM in two different applications: text classification and page recommendation. With respect to text classification, the topics were used to improve document collection representation. Concerning page recommendation, topics were used as contextual information in context aware recommender systems. Results have shown that the topics provided by the LARCM can be used to improve both applications. (C) 2018 Elsevier Ltd. All rights reserved.en
dc.description.affiliationUniv Sao Paulo, Inst Math & Comp Sci, Ave Trabalhador Sao Carlense 400, BR-13566590 Sao Carlos, SP, Brazil
dc.description.affiliationUniv Estadual Maringa, Dept Informat, Ave Colombo, BR-87020900 Maringa, Parana, Brazil
dc.description.affiliationState Univ Sao Paulo, Inst Geosci & Exact Sci, 24 A, BR-13506900 Rio Claro, SP, Brazil
dc.description.affiliationEmbrapa Agr Informat, Ave Dr Andre Tosello, BR-13083886 Campinas, SP, Brazil
dc.description.affiliationUnespState Univ Sao Paulo, Inst Geosci & Exact Sci, 24 A, BR-13506900 Rio Claro, SP, Brazil
dc.description.sponsorshipAraucaria Foundation (Parana/Brazil)
dc.description.sponsorshipCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.format.extent34-60
dc.identifierhttp://dx.doi.org/10.1016/j.eswa.2018.06.021
dc.identifier.citationExpert Systems With Applications. Oxford: Pergamon-elsevier Science Ltd, v. 112, p. 34-60, 2018.
dc.identifier.doi10.1016/j.eswa.2018.06.021
dc.identifier.issn0957-4174
dc.identifier.urihttp://hdl.handle.net/11449/186232
dc.identifier.wosWOS:000442708600003
dc.language.isoeng
dc.publisherElsevier B.V.
dc.relation.ispartofExpert Systems With Applications
dc.rights.accessRightsAcesso aberto
dc.sourceWeb of Science
dc.subjectDocument representation
dc.subjectTopic model
dc.subjectAssociation rules
dc.subjectClustering
dc.subjectText classification
dc.subjectContext-aware recommender systems
dc.titleLatent association rule cluster based model to extract topics for classification and recommendation applicationsen
dc.typeArtigo
dcterms.licensehttp://www.elsevier.com/about/open-access/open-access-policies/article-posting-policy
dcterms.rightsHolderElsevier B.V.
unesp.author.orcid0000-0002-8552-6655[3]

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