Think Before You Act: Intention-Guided Reasoning for LLM-Based Location Prediction
Researchers propose IntentPOI, a two-stage AI framework that improves next location prediction by first inferring user intentions before selecting specific points-of-interest. The method outperforms existing approaches by decoupling intention reasoning from location selection, addressing limitations in current LLM-based prediction systems.
IntentPOI addresses a fundamental limitation in how AI systems approach location prediction tasks. Traditional methods treat POI prediction as direct trajectory-to-location mapping, which causes models to rely heavily on shallow patterns and historical frequency bias rather than understanding why users visit specific locations. The researchers' key insight—that users first form travel intentions before selecting destinations—mirrors human decision-making processes and provides a more robust foundation for prediction algorithms.
The framework's two-stage architecture represents a meaningful evolution in location-based service technology. The thinking stage incorporates multiple behavioral signals including mobility patterns, peer behavior analysis, and temporal context to infer user intentions. The acting stage then leverages these inferred intentions to filter candidate locations and make more semantically meaningful predictions. This separation of concerns allows the model to reason about user motivations independently from location selection logic.
The practical implications extend across location-based services, recommendation systems, and urban computing applications. Improved location prediction directly benefits navigation apps, social platforms, retail analytics, and tourism services by reducing prediction errors and providing more contextually appropriate suggestions. Developers building LLM-powered location services gain a framework that achieves better performance while being more interpretable—users and analysts can understand that predictions are based on inferred travel intentions rather than opaque pattern matching.
Future development hinges on validating IntentPOI's generalization across diverse geographic regions, user demographics, and seasonal patterns. The framework's reliance on intention inference quality suggests that further refinement of the thinking stage through additional contextual features could yield additional performance gains.
- →IntentPOI decouples intention inference from location prediction, improving accuracy over direct trajectory-to-location mapping approaches
- →The two-stage framework incorporates mobility patterns, peer behaviors, and temporal context to understand user travel motivations
- →Extensive experiments across three real-world datasets demonstrate consistent improvements over eleven state-of-the-art baselines
- →The approach addresses shallow trajectory correlation and historical frequency bias problems in existing LLM-based prediction systems
- →Improved location prediction has direct applications for navigation, recommendation systems, retail analytics, and urban computing services