%0 Conference Paper %1 Sanchez_2022 %A Sanchez, Pablo %A Dietz, Linus W. %B Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization %D 2022 %I ACM %K clustering location-recommendation umap2022 %P 132-142 %R 10.1145/3503252.3531320 %T Travelers vs. Locals: The Effect of Cluster Analysis in Point-of-Interest Recommendation %U https://doi.org/10.1145%2F3503252.3531320 %X 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 together, 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 these locals 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 huge differences in the respective travelers groups, especially between predominantly domestic and international travelers. Due to the large 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.

@inproceedings{Sanchez_2022, abstract = {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 together, 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 these locals 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 huge differences in the respective travelers groups, especially between predominantly domestic and international travelers. Due to the large 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.}, added-at = {2022-07-07T16:08:19.000+0200}, author = {Sanchez, Pablo and Dietz, Linus W.}, biburl = {https://www.bibsonomy.org/bibtex/29643a614b96cbc1a2ffad0aa707a5178/brusilovsky}, booktitle = {Proceedings of the 30th {ACM} Conference on User Modeling, Adaptation and Personalization}, description = {Travelers vs. Locals: The Effect of Cluster Analysis in Point-of-Interest Recommendation | Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization}, doi = {10.1145/3503252.3531320}, interhash = {78176bc4656c57cfb8e4b88a25daab95}, intrahash = {9643a614b96cbc1a2ffad0aa707a5178}, keywords = {clustering location-recommendation umap2022}, month = jul, note = {Assessing the value of RecSys you need to distinguish user types - and it can be done by clustering}, pages = {132-142}, publisher = {{ACM}}, timestamp = {2022-07-07T16:08:19.000+0200}, title = {Travelers vs. Locals: The Effect of Cluster Analysis in Point-of-Interest Recommendation}, url = {https://doi.org/10.1145%2F3503252.3531320}, year = 2022 }