Can I read Forecasting Unseen Points of Interest Visits Using Context and Proximity Priors on EtoBox?

Forecasting Unseen Points of Interest Visits Using Context and Proximity Priors by Li, Ziyao; Hsu, Shang-Ling; Shahabi, Cyrus is a scholarly article available to read on EtoBox.

What is Forecasting Unseen Points of Interest Visits Using Context and Proximity Priors about?

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 introd

Author

Li, Ziyao; Hsu, Shang-Ling; Shahabi, Cyrus

Published

2024

Language

EN