IntentPOI: Transforming Location Predictions with Intention-Guided Reasoning

Predicting where a user will visit next is a cornerstone challenge for location-based services. Recent innovations have leaned on large language models, but a new framework called IntentPOI is shaking up the field by focusing on user intentions rather than merely mapping historical trajectories.

Understanding User Intentions

The key insight driving IntentPOI is the realization that users don't just choose locations haphazardly. Instead, they first form a traveling intention and then select specific Points-of-Interest (POIs) that align with that intent. This approach addresses the limitations of previous models that often fell prey to shallow trajectory correlations and biases based on historical frequency.

The paper, published in Japanese, reveals the two-stage process of IntentPOI. In the 'thinking' stage, the model infers users' intermediate intentions by analyzing historical mobility patterns, peer behaviors, and temporal contexts. This is followed by the 'acting' stage, where a compact candidate pool is constructed, and intention-guided reasoning identifies the most suitable locations.

Benchmarking Success

The benchmark results speak for themselves. Extensive experiments across three real-world datasets show IntentPOI consistently outperforming eleven state-of-the-art baselines. Compare these numbers side by side, and the advantage of focusing on intentions becomes clear. It's an approach that acknowledges the complexity of human decision-making rather than oversimplifying it into a mere trajectory-to-location task.

But why is this shift important? As AI becomes more integrated into services we use daily, understanding the why behind user decisions is important. If models can't grasp intentions, they risk missing the context that drives user behavior. Do we want AI systems that mimic shallow, reactive decision-making or ones that reflect deeper, intention-based reasoning?

Implications for Location-Based Services

Western coverage has largely overlooked this intention-guided shift, but the implications for location-based services are significant. By predicting not just where users will go, but why they're going there, businesses can tailor offerings more precisely. It's not just about knowing a user might visit a coffee shop but understanding they're seeking a relaxing atmosphere after a busy day.

IntentPOI's approach is a big deal for the industry. The data shows that when models understand intentions, they don't just predict better. They enhance user experiences and open new avenues for personalized services. As AI continues to evolve, the question isn't just about what's possible but about what truly serves the user. IntentPOI might just be the framework that sets the standard.