Disentangling the Factors Driving Friendship Formation: An LLM-Enhanced Graph Convolutional Approach for Friend Recommendation

Abstract

The global proliferation of social media has provided a unique platform for cross-cultural exchange, greatly enhancing interactions between users from different cultural backgrounds through friend recommendation systems. However, the highly complex and intrinsically coupled nature of factors driving friendship formation makes it difficult for traditional methods to effectively predict and recommend genuinely deep social connections. Therefore, this study proposes leveraging emerging information technologies, specifically deep learning, to optimize and improve friend recommendation systems on social media platforms. This paper introduces a novel personality trait disentanglement method. By using large language models to extract personality factors from user text, we constructed a multi-subgraph convolutional method driven by personality traits. This enables the model to clearly distinguish the mechanisms of different personality factors. Additionally, we designed a shared attention layer to adaptively learn the importance weights of different personality traits, and implicit representations to capture non-personality-driven factors. Our research combines deep learning with personality trait analysis to foster deeper interpersonal understanding and cultural exchange, thereby enhancing the quality and breadth of interactions on social networks globally