A Preliminary Investigation of User- and Item-Centered Bias in POI Recommendation

This study investigates the application of Recom-mender Systems (RS) to predict future Point of Interest (POI) visits based on check-in data, with a particular focus on biases related to individual mobility patterns and POI popularity. We conduct a comprehensive analysis by training and evaluating three RS models based on different architectures: a Convo-lutional Neural Network, an Attention-based Neural Network, and a Markov-based predictor. Our analysis reveals that POI recommenders: do not show bias in terms of the typical distance traveled by users but tend to favor less exploratory users, and are biased towards more popular POIs. Our findings highlight the potential of RS in capturing and forecasting user behavior, while also underscoring the need to mitigate these biases, thereby advancing the understanding of RS and their broader social impact.