A feedback-driven knowledge-graph recommendation framework for personalised service optimisation in offline commercial complexes

Purpose 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. Design/methodology/approach 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. Findings 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. Originality/value 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.