# Meta-Learning with Graph Community Detection for Cold-Start User Clustering
## 3 Related Works
### Meta-Learning Recommendation Algorithm Applying Clustering  
Recently, meta-learning-related methods have achieved remarkable success in the field of sampless learning [31]. As a result, many meta-learning-related recommendation models have been proposed to alleviate the cold-start problem. They consider users or items as tasks, record data as samples, and learn an optimal initialisation parameter by sharing the knowledge of all users, which can be quickly adapted to new users by a finite number of gradient descent steps. However, when solving the cold-start problem, meta-learning usually relies on extracting transferable patterns from a small quantity of data, while the clustering of rough data trends (cluster-based meta-learning) may become a key bottleneck for the following reasons:  
1. Data sparsity: the small quantity of data in the cold-start phase leads to unstable clustering (such as K-means, DBSCAN) results, which affects the construction of meta-tasks.
2. Noise sensitivity: meta-learning (such as MAML) relies on high-quality task distribution, but outliers (such as order-brushing behaviour, sensor error data) distort the cluster structure and cause the model to learn the wrong pattern.
3. Gradient contamination: the two-layer optimisation of meta-learning (inner-loop adaptation and outer-loop meta-update) is sensitive to noise, and abnormal samples may cause model parameter drift and reduce generalization ability.  
Due to the destructive impact of outliers on meta-learning and the high dependence of the cold-start phase on data quality, data cleaning is not an option but a must when outliers appear in the cold-start phase; otherwise, meta-learning may learn the wrong migration pattern. From existing research [32], we know that the key methods of data cleaning can be roughly divided into three categories: anomaly detection is used to remove obvious noise, robust meta-learning is used to deal with residual noise, and data augmentation is used to compensate for data loss. Among them, anomaly detection includes clustering methods, such as DBSCAN, density-based clustering that automatically identifies outliers (samples that cannot be classified into any cluster), and Isolation Forest, specialized for anomaly detection and suitable for high-dimensional sparse data. Despite their degree of success in mitigating the cold-start problem, most existing meta-learning algorithms assume that relocatable knowledge can be shared globally among all users; however, in real recommender systems, there are huge differences in user preferences, and information sharing among users with opposite interests is useless or even negatively affects performance. At the same time, the direction of gradient descent for users with different preferences varies greatly or is even opposite, which makes the initialisation parameters learned by existing meta-learning methods not optimal for most users. In order to solve the above problems, it is necessary to classify users under cold-start conditions. Most of the existing cold-start user classification methods look for highly distinguishable content and classify users based on their ratings of the content. However, these methods perform poorly in most cases as it is difficult to find such content due to the scarcity of new user rating data. Therefore, based on meta-learning, this paper proposes a new idea for user classification. Unlike existing meta-learning methods that directly learn global initialisation parameters for all users, this paper uses a clustering algorithm for graph community detection to classify users and customises unique optimal initialisation parameters for users with similar interests, which are more suitable for these users than directly learned global initialisation parameters and can be adapted to this interest cluster more quickly. In this way, the model can find users with similar interests and share knowledge only among them at a fine-grained level to improve recommendation results.