Taming Continuous Location Tokens for Generalized Next Point-of-Interest Recommendation

Next point-of-interest (POI) prediction is the task of estimating a user’s future geographical location, supporting applications from restaurant recommendation to real estate shopping. Many state-of-the-art methods assign discrete embeddings to each POI, ignoring spatial relationships and limiting knowledge transfer to nearby unseen locations. To address this limitation, our framework combines a continuous latitude–longitude encoder using random Fourier features (RFF) with a causal transformer that predicts continuous location representations from historical check-in sequences. On Massive-STEPS, a multi-city check-in dataset, we examine how RFF frequency bandwidth controls representation granularity: lower frequencies enforce smooth spatial priors, whereas higher frequencies encourage precise coordinate memorization. We illustrate this tradeoff by withholding a portion of locations from training and comparing the recall on both seen and unseen POIs: a lower-frequency RFF configuration raises average unseen Recall@100 across six city datasets from 36.4% to 42.9%, while seen recall falls from 63.8% to 57.5%. Further lowering the frequencies does not consistently improve unseen recall, indicating an optimal granularity among the tested settings. Additionally, we compare RFF with discrete location encoders and use a national-scale real estate shopping dataset to validate our approach. These findings provide empirical guidance for choosing the spatial scale of RFF embeddings so that learned geographic patterns generalize to destinations absent from training data.