Offline commercial complexes feature diverse services, sparse behavioural data and rapidly changing user interests, which limit conventional recommendation methods. This study develops a personalised recommendation framework designed for high-uncertainty offline environments and evaluates its performance across static and dynamic systems and across cold-start and stable user groups.
A lightweight system is built by encoding questionnaire data into a preference knowledge graph and applying a hybrid user similarity model for preference inference. A dynamic module updates user–category relations through explicit feedback and controlled semantic diffusion, enabling continuous refinement. Static and dynamic configurations are tested separately on cold-start and stable users and ablation experiments quantify the contribution of relation updating and diffusion.
The static system provides a strong baseline under sparse data. The dynamic system yields further improvements: for cold-start users, P@1 and HIT@3 rise by 5.6 and 6.6 percentage points; for stable users, P@1 increases by 10.0 percent with clear gains in mean reciprocal rank and discounted cumulative gain. Ablation results show that feedback-driven edge updating generates most improvements, while intra-cluster diffusion supplies additional benefits, particularly for cold-start users.
The study offers a structurally adaptable and computationally efficient framework for offline commercial environments without behavioural logs. Through a combined static-to-dynamic and cold-start-to-stable evaluation, it demonstrates how lightweight graph modelling and local feedback-based updates can deliver accurate, interpretable and deployable personalised recommendations for real facility and commercial management settings.
