Linus Dietz

Lecturer (Assistant Professor) in Computer Science Education

Understanding the Influence of Data Characteristics on the Performance of Point-of-Interest Recommendation Algorithms

1 minute read

This work is a joint project with Pablo Sánchez and Alejandro Bellogín, we investigated the effects of data characteristics on the performance of point-of-interest recommendation algorithms.

Point-of-interest (POI) recommenders have become ubiquitous tools for travelers, guiding them on where to eat, explore, or relax. But while researchers and developers often focus on which algorithm performs best there is little research on understanding why certain models perform better than others across datasets. In this study, we set out to investigate that using data characteristics. Using Foursquare check-in data from New York City, we developed a framework that links the characteristics of a dataset, such as sparsity, popularity bias, and user mobility, to the performance of different recommendation algorithms analyzing accuracy, novelty and item exposure. To uncover how data characteristics shape outcomes we curated 144 distinct data subsets, each reflecting real-world conditions: peak tourist seasons, domestic versus international users, and variations in user activity and mobility. We then trained a mix of classic and POI-specific recommendation models on each subset, analyzing the results using an explanatory framework powered by a multiple-regression model.

What we found confirmed our suspicions: widely studied data characteristics like density and popularity bias do play a role—but so do subtler dynamics, such as how far users tend to travel or how long they’ve been active on the platform. These hidden variables often determine whether a recommendation algorithm succeeds or falls short. Our findings reinforce a key insight: there’s no universal best model. The optimal algorithm depends on the specific characteristics of the data and the goals of the recommendation system, trading-off accuracy, novelty, or equitable item exposure. By quantifying the characteristics of the data set, one can better select or fine-tune models to meet those goals. Ultimately, we hope this work helps pave the way toward more relevant and serendipitous suggestions for users—and more balanced visibility for local businesses. Our framework represents a step toward more informed, intentional choices in recommendation system design to deliver recommendations that are relevant, but also fair, diverse, and genuinely helpful to both travelers and the communities they explore.

Linus W. Dietz, Pablo Sánchez, and Alejandro Bellogín. “Understanding the Influence of Data Characteristics on the Performance of Point-of-Interest Recommendation Algorithms”. In: Information Technology & Tourism 27.1 (Jan. 2025), pp. 75–124. doi: 10.1007/s40558-024-00304-0.

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