Personality-aware followee recommendation algorithms: An empirical analysis
- Autores
- Tommasel, Antonela; Corbellini, Alejandro; Godoy, Daniela Lis; Schiaffino, Silvia Noemi
- Año de publicación
- 2016
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- As the popularity of micro-blogging sites, expressed as the number of active users and volume of online activities, increases, the difficulty of deciding who to follow also increases. Such decision might not depend on a unique factor as users usually have several reasons for choosing whom to follow. However, most recommendation systems almost exclusively rely on only two traditional factors: graph topology and user-generated content, disregarding the effect of psychological and behavioural characteristics, such as personality, over the followee selection process. Due to its effect over people's reactions and interactions with other individuals, personality is considered as one of the primary factors that influence human behaviour. This study aims at assessing the impact of personality in the accurate prediction of followees, beyond simple topological and content-based factors. It analyses whether user personality could condition followee selection by combining personality traits with the most commonly used followee predictive factors. Results showed that an accurate appreciation of such predictive factors tied to a quantitative analysis of personality is crucial for guiding the search of potential followees, and thus, enhance recommendations.
Fil: Tommasel, Antonela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina
Fil: Corbellini, Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina
Fil: Godoy, Daniela Lis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina
Fil: Schiaffino, Silvia Noemi. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina - Materia
-
Followee Recommendation
Human Aspects Recommendation
Personality Traits
Twitter - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
- Repositorio
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/58470
Ver los metadatos del registro completo
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Personality-aware followee recommendation algorithms: An empirical analysisTommasel, AntonelaCorbellini, AlejandroGodoy, Daniela LisSchiaffino, Silvia NoemiFollowee RecommendationHuman Aspects RecommendationPersonality TraitsTwitterhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1As the popularity of micro-blogging sites, expressed as the number of active users and volume of online activities, increases, the difficulty of deciding who to follow also increases. Such decision might not depend on a unique factor as users usually have several reasons for choosing whom to follow. However, most recommendation systems almost exclusively rely on only two traditional factors: graph topology and user-generated content, disregarding the effect of psychological and behavioural characteristics, such as personality, over the followee selection process. Due to its effect over people's reactions and interactions with other individuals, personality is considered as one of the primary factors that influence human behaviour. This study aims at assessing the impact of personality in the accurate prediction of followees, beyond simple topological and content-based factors. It analyses whether user personality could condition followee selection by combining personality traits with the most commonly used followee predictive factors. Results showed that an accurate appreciation of such predictive factors tied to a quantitative analysis of personality is crucial for guiding the search of potential followees, and thus, enhance recommendations.Fil: Tommasel, Antonela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Corbellini, Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Godoy, Daniela Lis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Schiaffino, Silvia Noemi. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaPergamon-Elsevier Science Ltd2016-05info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/58470Tommasel, Antonela; Corbellini, Alejandro; Godoy, Daniela Lis; Schiaffino, Silvia Noemi; Personality-aware followee recommendation algorithms: An empirical analysis; Pergamon-Elsevier Science Ltd; Engineering Applications Of Artificial Intelligence; 51; 5-2016; 24-360952-1976CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0952197616000208info:eu-repo/semantics/altIdentifier/doi/10.1016/j.engappai.2016.01.016info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:47:30Zoai:ri.conicet.gov.ar:11336/58470instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 09:47:30.441CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse |
dc.title.none.fl_str_mv |
Personality-aware followee recommendation algorithms: An empirical analysis |
title |
Personality-aware followee recommendation algorithms: An empirical analysis |
spellingShingle |
Personality-aware followee recommendation algorithms: An empirical analysis Tommasel, Antonela Followee Recommendation Human Aspects Recommendation Personality Traits |
title_short |
Personality-aware followee recommendation algorithms: An empirical analysis |
title_full |
Personality-aware followee recommendation algorithms: An empirical analysis |
title_fullStr |
Personality-aware followee recommendation algorithms: An empirical analysis |
title_full_unstemmed |
Personality-aware followee recommendation algorithms: An empirical analysis |
title_sort |
Personality-aware followee recommendation algorithms: An empirical analysis |
dc.creator.none.fl_str_mv |
Tommasel, Antonela Corbellini, Alejandro Godoy, Daniela Lis Schiaffino, Silvia Noemi |
author |
Tommasel, Antonela |
author_facet |
Tommasel, Antonela Corbellini, Alejandro Godoy, Daniela Lis Schiaffino, Silvia Noemi |
author_role |
author |
author2 |
Corbellini, Alejandro Godoy, Daniela Lis Schiaffino, Silvia Noemi |
author2_role |
author author author |
dc.subject.none.fl_str_mv |
Followee Recommendation Human Aspects Recommendation Personality Traits |
topic |
Followee Recommendation Human Aspects Recommendation Personality Traits |
purl_subject.fl_str_mv |
https://purl.org/becyt/ford/1.2 https://purl.org/becyt/ford/1 |
dc.description.none.fl_txt_mv |
As the popularity of micro-blogging sites, expressed as the number of active users and volume of online activities, increases, the difficulty of deciding who to follow also increases. Such decision might not depend on a unique factor as users usually have several reasons for choosing whom to follow. However, most recommendation systems almost exclusively rely on only two traditional factors: graph topology and user-generated content, disregarding the effect of psychological and behavioural characteristics, such as personality, over the followee selection process. Due to its effect over people's reactions and interactions with other individuals, personality is considered as one of the primary factors that influence human behaviour. This study aims at assessing the impact of personality in the accurate prediction of followees, beyond simple topological and content-based factors. It analyses whether user personality could condition followee selection by combining personality traits with the most commonly used followee predictive factors. Results showed that an accurate appreciation of such predictive factors tied to a quantitative analysis of personality is crucial for guiding the search of potential followees, and thus, enhance recommendations. Fil: Tommasel, Antonela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina Fil: Corbellini, Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina Fil: Godoy, Daniela Lis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina Fil: Schiaffino, Silvia Noemi. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina |
description |
As the popularity of micro-blogging sites, expressed as the number of active users and volume of online activities, increases, the difficulty of deciding who to follow also increases. Such decision might not depend on a unique factor as users usually have several reasons for choosing whom to follow. However, most recommendation systems almost exclusively rely on only two traditional factors: graph topology and user-generated content, disregarding the effect of psychological and behavioural characteristics, such as personality, over the followee selection process. Due to its effect over people's reactions and interactions with other individuals, personality is considered as one of the primary factors that influence human behaviour. This study aims at assessing the impact of personality in the accurate prediction of followees, beyond simple topological and content-based factors. It analyses whether user personality could condition followee selection by combining personality traits with the most commonly used followee predictive factors. Results showed that an accurate appreciation of such predictive factors tied to a quantitative analysis of personality is crucial for guiding the search of potential followees, and thus, enhance recommendations. |
publishDate |
2016 |
dc.date.none.fl_str_mv |
2016-05 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://purl.org/coar/resource_type/c_6501 info:ar-repo/semantics/articulo |
format |
article |
status_str |
publishedVersion |
dc.identifier.none.fl_str_mv |
http://hdl.handle.net/11336/58470 Tommasel, Antonela; Corbellini, Alejandro; Godoy, Daniela Lis; Schiaffino, Silvia Noemi; Personality-aware followee recommendation algorithms: An empirical analysis; Pergamon-Elsevier Science Ltd; Engineering Applications Of Artificial Intelligence; 51; 5-2016; 24-36 0952-1976 CONICET Digital CONICET |
url |
http://hdl.handle.net/11336/58470 |
identifier_str_mv |
Tommasel, Antonela; Corbellini, Alejandro; Godoy, Daniela Lis; Schiaffino, Silvia Noemi; Personality-aware followee recommendation algorithms: An empirical analysis; Pergamon-Elsevier Science Ltd; Engineering Applications Of Artificial Intelligence; 51; 5-2016; 24-36 0952-1976 CONICET Digital CONICET |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0952197616000208 info:eu-repo/semantics/altIdentifier/doi/10.1016/j.engappai.2016.01.016 |
dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
eu_rights_str_mv |
openAccess |
rights_invalid_str_mv |
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
dc.format.none.fl_str_mv |
application/pdf application/pdf application/pdf application/pdf application/pdf application/pdf |
dc.publisher.none.fl_str_mv |
Pergamon-Elsevier Science Ltd |
publisher.none.fl_str_mv |
Pergamon-Elsevier Science Ltd |
dc.source.none.fl_str_mv |
reponame:CONICET Digital (CONICET) instname:Consejo Nacional de Investigaciones Científicas y Técnicas |
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Consejo Nacional de Investigaciones Científicas y Técnicas |
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CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas |
repository.mail.fl_str_mv |
dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar |
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13.13397 |