You Are What You Click: Web Interaction Analysis for User Profile Detection
- Autores
- Loza Bonora, Leonardo Germán; Grigera, Julián; Garrido, Alejandra
- Año de publicación
- 2025
- Idioma
- inglés
- Tipo de recurso
- documento de conferencia
- Estado
- versión publicada
- Descripción
- Evaluation of web user interaction serves various purposes like analysis and evolution of UX, but it requires significant resources. To reduce the time and cost associated with such assessments, several automated solutions have been developed to analyze interactions by capturing logs. However, many of these approaches assume that all users interact similarly, disregarding individual characteristics such as mouse and keyboard movement speeds. Additionally, they often overlook the time required for analysis. We propose that identifying a user’s interaction profile can enhance the quality of automated log analysis. To achieve this, in this proposal interactive user profiles on the web will be studied and defined. Through experimentation, we will analyze real user behavior and its relationship with these profiles. Finally, the detection of user profiles and interactive characteristics will be automated, considering key variables such as detection speed and prediction accuracy.
- Materia
-
Ciencias de la Computación e Información
Web Interaction
User Profiling
Log Analysis - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- http://creativecommons.org/licenses/by-nc-nd/4.0/
- Repositorio
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- Institución
- Comisión de Investigaciones Científicas de la Provincia de Buenos Aires
- OAI Identificador
- oai:digital.cic.gba.gob.ar:11746/12676
Ver los metadatos del registro completo
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You Are What You Click: Web Interaction Analysis for User Profile DetectionLoza Bonora, Leonardo GermánGrigera, JuliánGarrido, AlejandraCiencias de la Computación e InformaciónWeb InteractionUser ProfilingLog AnalysisEvaluation of web user interaction serves various purposes like analysis and evolution of UX, but it requires significant resources. To reduce the time and cost associated with such assessments, several automated solutions have been developed to analyze interactions by capturing logs. However, many of these approaches assume that all users interact similarly, disregarding individual characteristics such as mouse and keyboard movement speeds. Additionally, they often overlook the time required for analysis. We propose that identifying a user’s interaction profile can enhance the quality of automated log analysis. To achieve this, in this proposal interactive user profiles on the web will be studied and defined. Through experimentation, we will analyze real user behavior and its relationship with these profiles. Finally, the detection of user profiles and interactive characteristics will be automated, considering key variables such as detection speed and prediction accuracy.2025info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttps://digital.cic.gba.gob.ar/handle/11746/12676enginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-nd/4.0/reponame:CIC Digital (CICBA)instname:Comisión de Investigaciones Científicas de la Provincia de Buenos Airesinstacron:CICBA2026-03-26T11:18:36Zoai:digital.cic.gba.gob.ar:11746/12676Institucionalhttp://digital.cic.gba.gob.arOrganismo científico-tecnológicoNo correspondehttp://digital.cic.gba.gob.ar/oai/snrdmarisa.degiusti@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:94412026-03-26 11:18:36.96CIC Digital (CICBA) - Comisión de Investigaciones Científicas de la Provincia de Buenos Airesfalse |
| dc.title.none.fl_str_mv |
You Are What You Click: Web Interaction Analysis for User Profile Detection |
| title |
You Are What You Click: Web Interaction Analysis for User Profile Detection |
| spellingShingle |
You Are What You Click: Web Interaction Analysis for User Profile Detection Loza Bonora, Leonardo Germán Ciencias de la Computación e Información Web Interaction User Profiling Log Analysis |
| title_short |
You Are What You Click: Web Interaction Analysis for User Profile Detection |
| title_full |
You Are What You Click: Web Interaction Analysis for User Profile Detection |
| title_fullStr |
You Are What You Click: Web Interaction Analysis for User Profile Detection |
| title_full_unstemmed |
You Are What You Click: Web Interaction Analysis for User Profile Detection |
| title_sort |
You Are What You Click: Web Interaction Analysis for User Profile Detection |
| dc.creator.none.fl_str_mv |
Loza Bonora, Leonardo Germán Grigera, Julián Garrido, Alejandra |
| author |
Loza Bonora, Leonardo Germán |
| author_facet |
Loza Bonora, Leonardo Germán Grigera, Julián Garrido, Alejandra |
| author_role |
author |
| author2 |
Grigera, Julián Garrido, Alejandra |
| author2_role |
author author |
| dc.subject.none.fl_str_mv |
Ciencias de la Computación e Información Web Interaction User Profiling Log Analysis |
| topic |
Ciencias de la Computación e Información Web Interaction User Profiling Log Analysis |
| dc.description.none.fl_txt_mv |
Evaluation of web user interaction serves various purposes like analysis and evolution of UX, but it requires significant resources. To reduce the time and cost associated with such assessments, several automated solutions have been developed to analyze interactions by capturing logs. However, many of these approaches assume that all users interact similarly, disregarding individual characteristics such as mouse and keyboard movement speeds. Additionally, they often overlook the time required for analysis. We propose that identifying a user’s interaction profile can enhance the quality of automated log analysis. To achieve this, in this proposal interactive user profiles on the web will be studied and defined. Through experimentation, we will analyze real user behavior and its relationship with these profiles. Finally, the detection of user profiles and interactive characteristics will be automated, considering key variables such as detection speed and prediction accuracy. |
| description |
Evaluation of web user interaction serves various purposes like analysis and evolution of UX, but it requires significant resources. To reduce the time and cost associated with such assessments, several automated solutions have been developed to analyze interactions by capturing logs. However, many of these approaches assume that all users interact similarly, disregarding individual characteristics such as mouse and keyboard movement speeds. Additionally, they often overlook the time required for analysis. We propose that identifying a user’s interaction profile can enhance the quality of automated log analysis. To achieve this, in this proposal interactive user profiles on the web will be studied and defined. Through experimentation, we will analyze real user behavior and its relationship with these profiles. Finally, the detection of user profiles and interactive characteristics will be automated, considering key variables such as detection speed and prediction accuracy. |
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2025 |
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2025 |
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eng |
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