Meta-Learning Enhanced Recommendation for Cold-Start Tourist Cities: A Multi-Component Adaptive Framework

Abstract

In the context of a rapidly expanding tourism industry, cold-start tourist cities face significant challenges in promoting their destinations due to the absence of user data and mature recommendation systems. To address this, we propose a novel recommendation model that integrates four key components: meta-learning for knowledge transfer and adaptation, an attention-based feature mining mechanism, a dynamically weighted collaborative filtering extension, and a reinforcement learning-based feedback optimization module. Experimental results on five real-world cold-start datasets (Asia, Europe, Africa, South America, Oceania) show that our model consistently outperforms baseline models including Content-Based Recommendation (CBR), Collaborative Filtering (CF), Neural Collaborative Filtering (NCF), and Graph Neural Network-based Recommendation (GNN-Rec). Specifically, the proposed model achieves an average improvement of approximately 25% in recommendation accuracy over CBR and 35% over CF. On MAP metrics, it shows substantial gains ranging from 13% to 20% depending on the region and cold-start severity. Experimental results demonstrate that the proposed meta-learning model significantly outperforms baseline methods. On average, it achieves 61% higher accuracy than CBR and 138% higher than CF across five cold-start datasets. Specifically, in mild cold-start settings, accuracy gains reach approximately 75% over CBR and 211% over CF; in severe cold-start conditions, improvements increase to 125% and 462.5%, respectively. These results confirm the strong generalization capacity and adaptability of the proposed model. These findings demonstrate the effectiveness and generalizability of our approach in addressing cold-start recommendation problems in the tourism.