Recommendation system of authorities and content based on Twitter for language therapy through data mining techniques
Áreas de investigación: |
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Año: | 2018 | ||||
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Tipo de publicación: | Artículo en conferencia | Palabras clave: | data mining; recommender systems | ||||
Autores: | Diego Quisi Peralta, 8 16 | ||||||
Título del libro: | Congreso Bienal de IEEE Argentina (IEEE ARGENCON) | ||||||
Dirección: | San Miguel de Tucumán, Argentina | ||||||
Organización: | IEEE | Mes: | Junio | ||||
BibTex: |
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Abstract: | According to latest estimations of the World Health Organization (WHO), approximately 15% of persons have some disability. From this group, a significant percentage of persons present different kinds of communication and language disorders. Additionally, it is important mentioning that language supports other essential processes in children
development, such as learning, skills to interact with his/her
peers, and establishing relationships. On the other hand,
nowadays exist several digital platforms like blogs, twitter, and
in general, social networks, where experts and practitioners
contribute with contents related to speech and language
therapy. For these reasons, in this paper, we present a
recommender system to support the content filtering, identify
experts or interest groups related to communication disorders.
Our recommender uses data mining techniques to perform the
contents filtering, and a clustering approach to classifying which twitter contents are related to speech-language therapy
area. In order to validate our proposal, we have conducted
several tests with the support of a team of experts. The
achieved results show that the recommendations of the system
are useful, coherent, and understandable. |
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Descargar: ARGENCON_2018_paper_203.pdf
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