Continuous-Time Sequential Recommendation via Personalized Gated Neural Flow

Continuous-time sequential recommendation (CTSR) models continuously evolving user preferences from irregularly occurring interactions. Recent Neural ODE-based approaches show promise in capturing these dynamics, but their reliance on numerical integration significantly increase inference latency, entailing accuracy–efficiency trade-off. To address this limitation, we propose Personalized Gated Neural Flow (PGNF), a solver-free CTSR framework that directly parameterizes a flow map for continuous-time preference modeling. Through a temporal gating mechanism, PGNF evolves item states from a user's interaction history over time in a user-specific manner, then combines them to represent the user's evolving preferences. To complement these personalized dynamics with global collaborative signals, PGNF reflects information from an item co-occurrence graph into the initial item states. This graph captures relative temporal proximity between items within users’ interaction histories providing time-aware collaborative information without requiring additional computation cost at inference. Experiments on real-world datasets across diverse domains and scales demonstrate that PGNF achieves the state-of-the-art accuracy, improves computational efficiency, and substantially reduces inference latency compared to ODE-based approaches.