28th ACM Conference on User Modeling, Adaptation and Personalization - UMAP '20, Genoa (Italy) Online. 12-18 julio 2020
Resumen:
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.
Palabras clave: location-based social networks, neighbours, trajectory similarity
DOI: 
https://doi.org/10.1145/3340631.3394882
Publicado en UMAP'20, pp: 353-357, ISBN: 978-1-4503-7950-2
Fecha de publicación: 2020-07-12.
Cita:
S. Torrijos, A. Bellogín, P. Sánchez, Discovering related users in location-based social networks, 28th ACM Conference on User Modeling, Adaptation and Personalization - UMAP '20, Genoa (Italy) Online. 12-18 julio 2020. En: UMAP'20: Conference proceedings, ISBN: 978-1-4503-7950-2
