<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>1646-9895</journal-id>
<journal-title><![CDATA[RISTI - Revista Ibérica de Sistemas e Tecnologias de Informação]]></journal-title>
<abbrev-journal-title><![CDATA[RISTI]]></abbrev-journal-title>
<issn>1646-9895</issn>
<publisher>
<publisher-name><![CDATA[AISTI - Associação Ibérica de Sistemas e Tecnologias de Informação]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1646-98952020000500073</article-id>
<article-id pub-id-type="doi">10.17013/risti.40.73-88</article-id>
<title-group>
<article-title xml:lang="pt"><![CDATA[Estudo Comparativo de Abordagens para Sistemas de Recomendação baseados em Personalidade com uso do serviço IBM Watson Personality Insights]]></article-title>
<article-title xml:lang="en"><![CDATA[Comparative Study of Approaches for Recommender Systems based on Personality using IBM Watson Personality Insights]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Aguiar]]></surname>
<given-names><![CDATA[Janderson Jason B.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Araújo]]></surname>
<given-names><![CDATA[Joseana M. F. R. de]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Costa]]></surname>
<given-names><![CDATA[Evandro de B.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidade Federal de Campina Grande  ]]></institution>
<addr-line><![CDATA[Campina Grande PB]]></addr-line>
<country>Brazil</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidade Federal de Alagoas  ]]></institution>
<addr-line><![CDATA[Maceió AL]]></addr-line>
<country>Brazil</country>
</aff>
<pub-date pub-type="pub">
<day>31</day>
<month>12</month>
<year>2020</year>
</pub-date>
<pub-date pub-type="epub">
<day>31</day>
<month>12</month>
<year>2020</year>
</pub-date>
<numero>40</numero>
<fpage>73</fpage>
<lpage>88</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.pt/scielo.php?script=sci_arttext&amp;pid=S1646-98952020000500073&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.pt/scielo.php?script=sci_abstract&amp;pid=S1646-98952020000500073&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.pt/scielo.php?script=sci_pdf&amp;pid=S1646-98952020000500073&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="pt"><p><![CDATA[Resumo Considerar a personalidade dos usuários nos Sistemas de Recomendação pode proporcionar resultados mais relevantes. Neste estudo, foi analisado se, com os avanços na detecção da personalidade (inferência sem usar questionários), as abordagens de Filtragem Colaborativa baseadas em personalidade continuam melhorando a acurácia das abordagens tradicionais (baseadas essencialmente em ratings). Outrossim, foi analisado se há diferenças ao aplicar modelos distintos de personalidade. No experimento, foram considerados 1058 usuários do TripAdvisor e 10889 clientes da Amazon, com características de personalidade inferidas via IBM Watson Personality Insights. Os resultados indicaram a possibilidade de melhorar a acurácia ao empregar uma abordagem usando dados inferidos concernentes a algum dos modelos de personalidade analisados (Big Five, Needs e Values). Ademais, o modelo Values proporcionou resultados equivalentes ao Big Five (sem facetas); e, em termos gerais, não houve melhoria ao usar o Big Five incluindo dados das suas facetas (nem ao incluir dados dos outros modelos).]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract Considering the personality of users in Recommender Systems can provide more relevant results. In this study, we analyzed whether, with advances in personality detection (inference without using questionnaires), Collaborative Filtering approaches based on personality continue to improve the accuracy of traditional approaches (based essentially on ratings). Furthermore, we analyze whether there are differences when applying different personality models. We considered 1058 TripAdvisor users and 10889 Amazon customers in the experiment, with personality characteristics inferred via IBM Watson Personality Insights. The results indicated the possibility of improving accuracy by employing an approach using inferred data concerning any personality models analyzed (Big Five, Needs, and Values). Moreover, the Values &#8203;&#8203;model provided results equivalent to the Big Five model (without facets), and, in general terms, there was no improvement when using the Big Five model with data from its facets (nor when including data from the other models).]]></p></abstract>
<kwd-group>
<kwd lng="pt"><![CDATA[Sistemas de Recomendação]]></kwd>
<kwd lng="pt"><![CDATA[Filtragem Colaborativa]]></kwd>
<kwd lng="pt"><![CDATA[Recomendação baseada em Personalidade]]></kwd>
<kwd lng="pt"><![CDATA[Watson Personality Insights.]]></kwd>
<kwd lng="en"><![CDATA[Recommender Systems]]></kwd>
<kwd lng="en"><![CDATA[Collaborative Filtering]]></kwd>
<kwd lng="en"><![CDATA[Personality-based Recommendation]]></kwd>
<kwd lng="en"><![CDATA[Watson Personality Insights.]]></kwd>
</kwd-group>
</article-meta>
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