2026-07-22T08:35:48Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:18979 2024-03-06T11:03:07Z ddc:004 open_access

Predicting visit frequencies to new places (Short Paper) Wiedemann, Nina Hong, Ye Raubal, Martin Human mobility Visitation patterns Place recommendation Next location prediction Human mobility exhibits power-law distributed visitation patterns; i.e., a few locations are visited frequently and many locations only once. Current research focuses on the important locations of users or on recommending new places based on collective behaviour, neglecting the existence of scarcely visited locations. However, assessing whether a user will return to a location in the future is highly relevant for personalized location-based services. Therefore, we propose a new problem formulation aimed at predicting the future visit frequency to a new location, focusing on the previous mobility behaviour of a single user. Our preliminary results demonstrate that visit frequency prediction is a difficult task, but sophisticated learning models can detect insightful patterns in the historic mobility indicative of future visit frequency. We believe these models can uncover valuable insights into the spatial factors that drive individual mobility behaviour. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Nina Wiedemann and Ye Hong and Martin Raubal 2023 Is Part Of LIPIcs, Volume 277, 12th International Conference on Geographic Information Science (GIScience 2023) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.GIScience.2023.84 urn:nbn:de:0030-drops-189794 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.84 eng https://creativecommons.org/licenses/by/4.0/legalcode