<?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>2304-0106</journal-id>
<journal-title><![CDATA[Anales de la Academia de Ciencias de Cuba]]></journal-title>
<abbrev-journal-title><![CDATA[Anales de la ACC]]></abbrev-journal-title>
<issn>2304-0106</issn>
<publisher>
<publisher-name><![CDATA[Academia de Ciencias de Cuba]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2304-01062022000100014</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Métodos de procesamiento de datos para sistemas recomendadores de filtrado colaborativo]]></article-title>
<article-title xml:lang="en"><![CDATA[Contributions to data preprocessing for collaborative filtering recommender systems]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Yera Toledo]]></surname>
<given-names><![CDATA[Raciel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
<xref ref-type="aff" rid="Aaf"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Caballero Mota]]></surname>
<given-names><![CDATA[Yailé]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
<xref ref-type="aff" rid="Aaf"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Castro Gallardo]]></surname>
<given-names><![CDATA[Jorge]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Martínez López]]></surname>
<given-names><![CDATA[Luis]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad de Ciego de Ávila  ]]></institution>
<addr-line><![CDATA[ Ciego de Ávila]]></addr-line>
<country>Cuba</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad de Camagüey  ]]></institution>
<addr-line><![CDATA[ Camagüey]]></addr-line>
<country>Cuba</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Universidad de Jaén  ]]></institution>
<addr-line><![CDATA[ Jaén]]></addr-line>
<country>Spain</country>
</aff>
<aff id="Af4">
<institution><![CDATA[,Titular de la Academia de Ciencias de Cuba  ]]></institution>
<addr-line><![CDATA[ La Habana]]></addr-line>
<country>Cuba</country>
</aff>
<aff id="Af5">
<institution><![CDATA[,Joven Asociado de la Academia de Ciencias de Cuba  ]]></institution>
<addr-line><![CDATA[ La Habana]]></addr-line>
<country>Cuba</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>04</month>
<year>2022</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>04</month>
<year>2022</year>
</pub-date>
<volume>12</volume>
<numero>1</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_arttext&amp;pid=S2304-01062022000100014&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_abstract&amp;pid=S2304-01062022000100014&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_pdf&amp;pid=S2304-01062022000100014&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN  Introducción:  Los sistemas recomendadores ayudan a los usuarios a encontrar aquella información que mejor se corresponda con sus necesidades y preferencias, en un espacio de búsqueda sobrecargado de posibles opciones. Se han detectado muy pocos trabajos enfocados en preprocesar datos inconsistentes en sistemas recomendadores, con vistas a elevar su eficacia.  Métodos:  El objetivo de esta investigación es desarrollar nuevos métodos de preprocesamiento para la eliminación de inconsistencias de tipo ruido natural en sistemas recomendadores de filtrado colaborativo, que contribuyan a mejorar la eficacia de las recomendaciones generadas. Varios de los métodos propuestos como parte de la investigación, se apoyan en el uso de la lógica difusa. Como resultado se obtuvieron 4 nuevos métodos de preprocesamiento de datos para sistemas recomendadores de filtrado colaborativo, tanto para recomendadores individuales como grupales.  Resultados:  Los métodos propuestos fueron evaluados utilizando bases de datos internacionales concebidas con este fin, verificando su eficacia. En adición estos métodos se introdujeron en un escenario real de recomendación que es el de los jueces en línea de programación en Cuba, mostrándose una mejora en la eficacia de la recomendación en este escenario. Estos resultados fueron publicados en 8 artículos Grupo I MES, incluyendo revistas Web of Science de alto factor de impacto como Knowledge-Based Systems y Decision Support Systems. Conclusiones. Los métodos propuestos son, por tanto, integrables a cualquier sistema recomendador de filtrado colaborativo, siendo capaces de mejorar la eficacia de los mismos. Se evidencian además, aplicaciones de los mismos a escenarios como el de los jueces en línea de programación.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT  Introduction: Recommender systems are focused on helping users to find the information that best fits their preferences and needs, in a search space overloaded with possible options. Most of the research works in this area are focused on proposing new recommendation approaches that improve the accuracy of previous works.  Methods: Based on this idea, the objective of this research is the development of new data preprocessing methods for removing natural noise in collaborative filtering recommender systems, in order to improve the accuracy of the generated recommendations. Several of the approaches proposed in this research are based on fuzzy logic for managing the uncertainty associated with the user&#8217;s preferences as a relevant part of recommender systems. As a result, four new approaches for data preprocessing methods in collaborative recommender systems were obtained, both for individual and group recommendation.  Results: The methods proposed were evaluated using international databases created for this purpose, thus verifying their accuracy. Furthermore, these methods were applied in a real recommendation situation associated with Programming Online Judges in Cuba, evidencing an improvement in the recommendation accuracy in such context. These results were published in 8 Group I papers, in Web of Science Journals such as Knowledge-Based Systems and Decision Support Systems. Conclusions-The methods proposed then can be integrated into any collaborative filtering recommendation system, in order to improve its accuracy. Such methods have been clearly applied to situations such as programming online judges.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[recomendación]]></kwd>
<kwd lng="es"><![CDATA[inconsistencias]]></kwd>
<kwd lng="es"><![CDATA[preferencias de los usuarios]]></kwd>
<kwd lng="es"><![CDATA[ruido natural]]></kwd>
<kwd lng="en"><![CDATA[recommendation]]></kwd>
<kwd lng="en"><![CDATA[inconsistencies]]></kwd>
<kwd lng="en"><![CDATA[user&#8217;s preferences]]></kwd>
<kwd lng="en"><![CDATA[natural noise]]></kwd>
</kwd-group>
</article-meta>
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</name>
<name>
<surname><![CDATA[Yang]]></surname>
<given-names><![CDATA[B]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[The research of the recommendation algorithm in online learning]]></article-title>
<source><![CDATA[International Journal of Multimedia and Ubiquitous Engineering]]></source>
<year>2015</year>
<volume>10</volume>
<numero>4</numero>
<issue>4</issue>
<page-range>71-80</page-range></nlm-citation>
</ref>
</ref-list>
</back>
</article>
