Spatio-Temporal Intent Modeling for Sequential Recommendation

Users' behaviors on recommendation platforms are typically driven by evolving intentions. Existing sequential recommendation models have two main limitations in capturing these intentions: insufficient modeling of higher-order relationships between prefix sequences and target items, and lack of effective mechanisms for capturing complex temporal dependencies. To address these challenges, we propose a Spatio-Temporal Intent Modeling framework (STIRec) that enhances recommendations through spatial and temporal dimensions. Our key innovations include: (1) a Multi-Hop Intent Aggregation mechanism that constructs a Spatial Intent Graph modeling three types of relationships (prefix-target, prefix-prefix, target-target), capturing common intent patterns through graph neural networks from a global perspective; (2) a Multi-Span Self-Attention module that fuses long and short-term query information to comprehensively model user behaviors and evolving intentions across temporal dimensions. These complementary mechanisms work together to understand user intent better, integrating global contextual patterns and temporal evolution dynamics. Experiments on five public datasets show that STIRec outperforms state-of-the-art methods by an average of 9.78% in recommendation accuracy, with enhanced robustness against noisy data. Source code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/theshy877/STIRec</uri>.