Forecasting Unseen Points of Interest Visits Using Context and Proximity Priors

TL;DR

This paper introduces a novel approach for predicting future Points of Interest (POIs) that includes unseen locations by leveraging semantic context and proximity priors, outperforming traditional classifiers in accuracy and robustness.

Contribution

The paper presents a new model that predicts semantic context first and then determines specific POIs, enabling the prediction of unseen POIs based on context and proximity.

Findings

Achieves 17% higher accuracy than baseline methods.

Remains robust with lower accuracy decline as new POIs emerge.

Outperforms existing models in predicting unseen POIs.

Abstract

Understanding human mobility behavior is crucial for numerous applications, including crowd management, location-based recommendations, and the estimation of pandemic spread. Machine learning models can predict the Points of Interest (POIs) that individuals are likely to visit in the future by analyzing their historical visit patterns. Previous studies address this problem by learning a POI classifier, where each class corresponds to a POI. However, this limits their applicability to predict a new POI that was not in the training data, such as the opening of new restaurants. To address this challenge, we propose a model designed to predict a new POI outside the training data as long as its context is aligned with the user's interests. Unlike existing approaches that directly predict specific POIs, our model first forecasts the semantic context of potential future POIs, then combines this with a proximity-based prior probability distribution to determine the exact POI. Experimental results on real-world visit data demonstrate that our model outperforms baseline methods that do not account for semantic contexts, achieving a 17% improvement in accuracy. Notably, as new POIs are introduced over time, our model remains robust, exhibiting a lower decline rate in prediction accuracy compared to existing methods.