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.
Resumen divulgativo:
En este artículo, analizamos cuatro sesgos distintos en el contexto de la recomendación de POIs y proponemos dos enfoques para mitigarlos. En el paper, demostramos que nuestras propuestas son capaces de mejorar la eficacia de las recomendaciones a la vez que alivian algunos de estos sesgos.
Palabras Clave: POI recommendation · Bias mitigation · Polarization · Temporal evaluation
Índice de impacto JCR y cuartil WoS: 4,800 - Q2 (2022)
Referencia DOI:
https://doi.org/10.1007/s10618-022-00913-5
Publicado en papel: Septiembre 2023.
Publicado on-line: Febrero 2023.
Cita:
P. Sánchez, A. Bellogín, L. Boratto, Bias characterization, assessment, and mitigation in location-based recommender systems. Data Mining and Knowledge Discovery. Vol. 37, pp. 1885 - 1929, Septiembre 2023. [Online: Febrero 2023]
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