User preference and social relationship-aware recommendations base on a novel light graph convolutional network

Within the realm of social recommendation, a recommender system can enhance its performance through the use of social information among users. Due to the abundance of redundant information in user interactions and social connections, it affects the performance of recommendation results negatively. Existing recommendation models do not distinguish the influence of different users and different friends. To solve this problem, this paper introduces a new recommendation framework, user preference and social relationship-aware light graph convolutional networks (USLGCN). The proposed framework distinguishes between users based on their interactions with items and social relationships to enhance recommendation accuracy. Specifically, we design a subgraph classification strategy that divides the user–item interaction graph and social graph into different subgraphs to capture the impact of various user types on items and friends, thereby reducing negative information and enhancing model resilience. On top of that, we also design a graph fusion module that enhances recommendation performance by fusing data from multiple subgraphs together. Experiments on public datasets show that USLGCN exhibits a 2.6% increase in recall accuracy compared to other social recommendation methods.