Travelers vs. Locals: The Effect of Cluster Analysis in Point-of-Interest Recommendation

This work is a joint project with Pablo Sánchez Pérez, which was very interesting as is brought together my previous work on traveler mobility analysis with the algorithmic analysis of point-of-interest recommendations. The article is published in the Proceedings of the 2022 ACM Conference on User Modeling, Adaptation and Personalization.

The involvement of geographic information differentiates point-of-interest recommendation from traditional product recommendation. This geographic influence is usually manifested in the effect of users tending toward visiting nearby locations, but further mobility patterns can be used to model different groups of users. In this study, we characterize the check-in behavior of local and traveling users in a global Foursquare check-in data set. Based on the features that capture the mobility and preferences of the users, we obtain representative groups of travelers and locals through an independent cluster analysis. Interestingly, for locals, the mobility features analyzed in this work seem to aggravate the cluster quality, whereas these signals are fundamental in defining the traveler clusters. To measure the effect of such a cluster analysis when categorizing users, we compare the performance of a set of recommendation algorithms, first on all users and then on each user group separately in terms of ranking accuracy, novelty, and diversity. Our results on the Foursquare data set of 139,270 users in five cities show that locals, despite being the most numerous groups of users, tend to obtain lower values than the travelers in terms of ranking accuracy while they also seem to receive more novel and diverse POI recommendations. For travelers, we observe the advantages of popularity-based recommendation algorithms in terms of ranking accuracy by recommending venues related to transportation and large commercial establishments. However, there are considerable differences in the respective traveler groups, especially between predominantly domestic and international travelers. Due to the considerable influence of mobility on the recommendations, this article underlines the importance of analyzing user groups differently when making and evaluating personalized point-of-interest recommendations.

Pablo Sánchez and Linus W. Dietz. “Travelers vs. Locals: The Effect of Cluster Analysis in Point-of-Interest Recommendation.” In: 30th ACM Conference on User Modeling, Adaptation and Personalization. UMAP’22. New York, NY, USA: ACM, July 2022, pp. 132– 142. DOI: 10.1145/3503252.3531320