# Meta-Learning with Graph Community Detection for Cold-Start User Clustering
###### Abstract  
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. old-start recommendation; recommendation models; personalised recommendation; graph community detection; meta-learning +
Footnote †: journal: Applied sciences  
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recommendation system is a necessary tool for the profitability and development of the contemporary Internet. With the development of various service websites and software, new users and new content are constantly being added, which poses a great challenge to traditional recommender systems. Traditional recommender systems [4] use a large number of user-content interaction histories to achieve knowledge of user preferences and understanding of content and find the right users for the content. However, when new users and content are added, since they have little or no interaction history, this makes traditional methods unable to understand user preferences and content, which leads to lower accuracy of recommendation results [5; 6]. For example, in the most classical collaborative filtering algorithm [1], the algorithm constructs a co-occurrence matrix from the interaction history of the user and the content, which contains the user's rating of the content. When it is necessary to recommend to the user, the algorithm extracts the co-occurrence signals to find some other users with similar interests as the current user, estimates the possible scores of the current user for the current content through the similarity between users and the scores of the other users for the current content, and ranks the content collection according to the obtained estimated scores of the users for the content, so as to recommend content that the user is most likely to like to the users. However, when new users and new content are added to the system, since they have little or almost no interaction history, this makes the co-occurrence matrix very sparse, and the model is unable to obtain effective co-occurrence signals so as to find other similar users and similar content, let alone produce accurate score predictions, and thus is unable to complete the subsequent steps to find the content that the users are likely to like. Related improved algorithms such as matrix decomposition also fail to obtain accurate rating predictions because the data are too sparse to compute an accurate vector representation. With the development of deep learning, recommender systems also ushered into a new development era [7].