Discovering Related Users in Location-based Social Networks
Entidad
UAM. Departamento de Ingeniería InformáticaEditor
Association for Computing MachineryFecha de edición
2020-07-13Cita
10.1145/3340631.3394882
UMAP '20: 28th ACM Conference on User Modeling, Adaptation and Personalization 8 (2020): 353-357
ISBN
9781450368612DOI
10.1145/3340631.3394882Financiado por
This work has been funded by the Ministerio de Ciencia, Innovación y Universidades (reference TIN2016-80630-P) and by the European Social Fund (ESF), within the 2017 call for predoctoral contracts. The first author also acknowledges the Ministerio de Educación y Formación Profesional, within the 2019-2020 call for collaboration grantsProyecto
Gobierno de España. TIN2016-80630-PVersión del editor
https://doi.org/10.1145/3340631.3394882Materias
location-based social networks; neighbours; trajectory similarity; InformáticaNota
© ACM 2020 This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in UMAP '20: Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization, https://doi.org/10.1145/3340631.3394882Derechos
© Association for Computring MachineryResumen
Users from Location-Based Social Networks can be characterised by how and where they move. However, most of the works that exploit this type of information neglect either its sequential or its geographical properties. In this article, we focus on a specific family of recommender systems, those based on nearest neighbours; we define related users based on common check-ins and similar trajectories and analyse their effects on the recommendations. For this purpose, we use a real-world dataset and compare the performance on different dimensions against several state-of-the-art algorithms. The results show that better neighbours could be discovered with these approaches if we want to promote novel and diverse recommendations
Lista de ficheros
Google Scholar:Torrijos, Sergio
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Bellogin Kouki, Alejandro
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Sánchez, Pablo
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