# Meta-Learning with Graph Community Detection for Cold-Start User Clustering
###### Abstract
After the introduction of deep learning algorithms, the recommendation efficiency and accuracy of recommendation systems have been significantly improved, but the cold-start problem (that is, the recommendation problem of new users and new content) is still a key challenge to be solved. Deep learning-related algorithms mainly use deep networks to learn a user vector representation and content vector representation through user and content interaction data [8], after which the user's predicted rating of the content is obtained by inputting the vector representations of the user and the content into a trained deep network. However, when new users or new items are added to the system, traditional recommendation algorithms often struggle to provide accurate recommendations due to the lack of historical interaction data, a problem known as the cold-start problem [9]. The interplay between the cold-start problem, large user bases, and related deriving issues can be comprehensively examined from the following perspectives:  
1. The amplification effect of data sparsity: When the number of users reaches tens of millions (for instance, TikTok (Beijing, China)'s global monthly active users total 1.5 billion), a 1% increase in new users translates into 1.5 million individuals encountering recommendation system failures. Observational studies indicate that new users on e-commerce platforms achieve only 23% in first-order conversion rate compared to returning users (as reported in Alibaba (Hangzhou, China)'s 2022 annual report). This \"data scarcity trap\" escalates exponentially as the scale of the platform grows. 2. The privacy paradox of cross-domain migration: The cross-platform data integration adopted to address the cold-start problem triggers privacy compliance risks when the user scale exceeds a critical point (approximately 500 million). Meta's cross-domaintracking tool led to a GDPR fine of 746 million euros in 2023, demonstrating that the scale effect amplifies legal risks. 3. Research on user loyalty indicates that when users are aware that their data are being utilised for cross-domain recommendations, the risk of attrition among high-value users (ARPU > $20) increases by 2.4 times ([PERSON] (Stamford, CT, USA) 2023). For service software and websites, when new users do not receive a good enough user experience when they are first contacted, i.e., they do not obtain the recommended content they want, it is very easy for the software and websites to lose these users, which seriously hinders the user growth and future development of these software tools and websites and even causes serious economic losses. For new content, the cold-start problem can prevent the content from getting enough exposure and create a serious long-tail effect. Therefore, effectively solving the cold-start problem [10] can help recommender-based platforms to be more attractive to new users, make better use of new content, achieve better user growth, and bring more economic benefits to related companies. In recent years, meta-learning has been introduced to the cold-start problem of recommender systems as a technique capable of quickly adapting to new tasks across multiple learning tasks, showing potential benefits. Most existing meta-learning algorithms assume that a priori knowledge can be shared globally among all users. However, information sharing among users with vastly different interests is ineffective and negative, which leads to suboptimal solutions of the model. Therefore, it is important and promising to improve meta-learning models in order to make the cold-start problem in recommender systems effectively mitigated. The contributions of our work are summarized below:  
1. In this paper, we introduce a graph community detection clustering algorithm for user cluster classification, thereby improving the effectiveness of meta-learning, experimentally verifying the effectiveness of our new idea in improving the performance of cold-start recommendations. 2.