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Bias characterization, assessment, and mitigation in location-based recommender systems

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Fecha
14/02/2023
Autor
Sánchez Pérez, Pablo
Bellogín, Alejandro
Boratto, Ludovico
Estado
info:eu-repo/semantics/publishedVersion
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Resumen
 
 
Location-Based Social Networks stimulated the rise of services such as Location-based Recommender Systems. These systems suggest to users points of interest (or venues) to visit when they arrive in a specific city or region. These recommendations impact various stakeholders in society, like the users who receive the recommendations and venue owners. Hence, if a recommender generates biased or polarized results, this affects in tangible ways both the experience of the users and the providers’ activities. In this paper, we focus on four forms of polarization, namely venue popularity, category popularity, venue exposure, and geographical distance. We characterize them on different families of recommendation algorithms when using a realistic (temporal-aware) offline evaluation methodology while assessing their existence. Besides, we propose two automatic approaches to mitigate those biases. Experimental results on real-world data show that these approaches are able to jointly improve the recommendation effectiveness, while alleviating these multiple polarizations.
 
URI
10.1007/s10618-022-00913-5
http://hdl.handle.net/11531/77410
Bias characterization, assessment, and mitigation in location-based recommender systems
Tipo de Actividad
Artículos en revistas
ISSN
1384-5810
Materias/ categorías / ODS
Instituto de Investigación Tecnológica (IIT)
Palabras Clave

POI recommendation · Bias mitigation · Polarization · Temporal evaluation
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Repositorio de la Universidad Pontificia Comillas copyright © 2015  Desarrollado con DSpace Software
Contacto | Sugerencias