# Meta-Learning with Graph Community Detection for Cold-Start User Clustering
## 2 Analysis of the Current Status of the Art and Its Shortcomings  
Due to the significant role of cold-start recommendations in user growth, there is an increasing focus on cold-start related algorithms. The primary cause of the cold-start problem stems from insufficient information, resulting in a lack of knowledge about the user and content within the recommender system. Early solutions primarily relied on edge information, such as gender, region, age, behaviour on other social platforms, and social relationships. However, these approaches did not yield satisfactory results due to the difficulty in mining edge information, which consumes substantial resources and raises privacy concerns. As small-data learning methods have gained traction in computer vision with promising outcomes, they have gradually been applied to address the cold-start recommendation problem. Meta-learning, comparative learning, and generative adversarial networks are widely employed algorithms that have demonstrated success across various cold-start scenarios. These approaches do not necessitate large quantities of labelled data; instead they can achieve desirable recommendation effects by leveraging limited user interaction data effectively mitigating the challenges posed by cold-start issues. Existing research methods can be broadly classified into two categories: data-based methods and small-data learning model methods. Among the data-based approaches, e.g., cross-domain migration techniques, heterogeneous graphs were introduced to mitigate the cold-start recommendation problem [11], and although these approaches mitigate the cold-start problem to some extent, the resultsare still not good enough. For cross-domain methods, it is very difficult to obtain information about another domain in practical situations, and for methods utilising similar users and content, since there is only a very small amount of information about both the users and the content in a cold-start situation, this makes it very difficult to obtain a very accurate classification of the users in the previous methods as well. Because of its unique multi-modal fusion capability and semantic sensing relational reasoning mechanism, the graph-based method can make use of easily obtained and abundant implicit information, which has great advantages over previous methods. But the lack of sufficient supervised signal guidance models in cold-start scenarios makes the graph structure-based methods unable to accurately utilise the mined user and content information, and the generated vector representations are still not accurate enough. Among the methods for learning models based on small data, the GAN (Generative Adversarial Network) is a relatively early proposed method for small data learning, which mainly solves the problem of insufficient training data in the case of small data by generating suitable virtual data close to the real data [12] and thus is also used to alleviate the cold-start problem in the recommendation domain. Contrastive learning is also a common learning method for small data representations, which can generate better vector representations with less supervised signals or even no supervision at all by mining the features of the data itself. Meta-learning, also known as learning to learn, aims to learn a common prior knowledge for all similar tasks [13] so that a small number of samples can be quickly adapted to a new task. Due to its prominence in the field of small-data image recognition, attempts have been made to use it to solve the cold-start recommendation problem [14]. Meta-learning has demonstrated promising potential in mitigating the cold-start problem through leveraging user content information and effectively circumvents overfitting issues by using a finite number of gradient descent steps [15]. However, conventional meta-learning algorithms only rely on the similarities among users, without considering the differences among users [16].