TL;DR
A novel SIoT-centered RLS framework based on dynamic user-service heterogeneous graphs is proposed, enabling integrated modeling of historical interactions and social influence for intelligent service and route recommendation.
Point-of-interest (POI) recommendation in location-based social networks (LBSNs) predicts a user’s next visit using check-in records. As critical factors influencing user decision making, geographic and social contexts are incorporated to deliver higher-quality recommendation services. However, current methods suffer f...
Digital Twins have become a central tool for Smart City planning and management, enabling simulation and analysis of complex urban systems such as mobility and energy infrastructures. However, most existing implementations primarily focus on physical and operational layers, relying on static rules or aggregate models t...
This work introduces SocialBuddy, the first agentic search framework tailored for social scenarios, and constructs SocialEnv, the first large-scale simulated environment for social search, which delivers multi-scale guidance in complex long-sequence scenarios.
Social Graph Mamba is proposed, a novel architecture that replaces attention-based social reasoning with Selective State Space Models (SSMs) operating on dynamically constructed interaction graphs that demonstrate competitive performance with linear sequence complexity compared to quadratic attention-based methods.
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