Understanding the influence of data characteristics on the performance of point-of-interest recommendation algorithms
Linus W. Dietz (),
Pablo Sánchez () and
Alejandro Bellogín ()
Additional contact information
Linus W. Dietz: King’s College London
Pablo Sánchez: Instituto de Investigación Tecnológica (IIT), Universidad Pontificia Comillas
Alejandro Bellogín: Universidad Autónoma de Madrid
Information Technology & Tourism, 2025, vol. 27, issue 1, No 3, 75-124
Abstract:
Abstract Point-of-interest (POI) recommendations are essential for travelers and the e-tourism business. They assist in decision-making regarding what venues to visit and where to dine and stay. While it is known that traditional recommendation algorithms’ performance depends on data characteristics like sparsity, popularity bias, and preference distributions, the impact of these data characteristics has not been systematically studied in the POI recommendation domain. To fill this gap, we extend a previously proposed explanatory framework by introducing new explanatory variables specifically relevant to POI recommendation. At its core, the framework relies on having subsamples with different data characteristics to compute a regression model, which reveals the dependencies between data characteristics and performance metrics of recommendation models. To obtain these subsamples, we subdivide a POI recommendation data set on New York City and measure the effect of these characteristics on different classical POI recommendation algorithms in terms of accuracy, novelty, and item exposure. Our findings confirm the crucial role of key data features like density, popularity bias, and the distribution of check-ins in POI recommendation. Additionally, we identify the significance of novel factors, such as user mobility and the duration of user activity. In summary, our work presents a generic method to quantify the influence of data characteristics on recommendation performance. The results not only show why certain POI recommendation algorithms excel in specific recommendation problems derived from a LBSN check-in data set in New York City, but also offer practical insights into which data characteristics need to be addressed to achieve better recommendation performance.
Keywords: Point-of-interest recommendation; Offline evaluation; Regression analysis; Data characteristics (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:
Downloads: (external link)
http://link.springer.com/10.1007/s40558-024-00304-0 Abstract (text/html)
Access to the full text of the articles in this series is restricted.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:spr:infott:v:27:y:2025:i:1:d:10.1007_s40558-024-00304-0
Ordering information: This journal article can be ordered from
http://www.springer. ... ystems/journal/40558
DOI: 10.1007/s40558-024-00304-0
Access Statistics for this article
Information Technology & Tourism is currently edited by Zheng Xiang
More articles in Information Technology & Tourism from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().