Meta-Learning with Graph Community Detection for Cold-Start User Clustering

The cold-start problem remains a significant challenge in recommendation systems, particularly in scenarios involving new users or items with insufficient historical interaction data, which severely limits the effectiveness of personalised recommendations. Despite extensive research efforts dedicated to addressing this issue, existing meta-learning approaches, while promising, often rely on the assumption that prior knowledge can be globally shared across all users. This assumption overlooks the inherent inefficiency of information sharing due to diverse user interests, frequently resulting in suboptimal solutions and constrained model performance. To address this limitation, we propose an enhanced meta-learning framework that leverages graph community detection algorithms to cluster users, enabling the extraction of unique prior knowledge within each cluster. This knowledge is then shared efficiently among users with similar interests within the same cluster. Through comparative experiments on cold-start recommendation tasks, our proposed model demonstrates superior performance over traditional methods, validating its effectiveness in improving cold-start recommendation accuracy. Furthermore, this study highlights potential application scenarios and future research directions for advancing cold-start solutions in recommendation systems.