Uncertainty-Aware Gated Context Fusion for Next POI Recommendation

Abstract

Next Point-of-Interest (POI) recommendation requires modeling both sequential user behavior and rich contextual signals such as venue category and visit timing. While sequential models effectively capture visit patterns, they often underutilize these cues or rely on fixed-weight fusion that fails to account for varying informativeness across categories. We propose a modular gated fusion framework that integrates item, category, and temporal embeddings into any sequential backbone, with an uncertainty-aware gating mechanism that adaptively controls the contribution of contextual information based on category-level embedding variance. Experiments on Foursquare NYC/TKY and Yelp demonstrate consistent HR@K and NDCG@K improvements over strong baselines across multiple sequential recommendation architectures. We further analyze the learned gates to reveal when and how contextual signals contribute to next-POI prediction.