A unified hypergraph framework for inter and intra-session dynamics in session-based social recommendations

Session-based recommendations have become increasingly important in social media platforms due to the dynamic and temporal nature of user interactions. The utilization of Graph Neural Networks in these systems has grown due to their proficiency in incorporating node information and structural topology. However, current recommendation methods that use graphs focus on recommendations within a single session, neglecting the more complex dependencies between different sessions. This omission limits improvements in the accuracy of recommendations. In addition, existing GNN-based approaches generally focus on simple binary connections, neglecting to capture the intricate and heterogeneous interactions in real-world situations. Furthermore, a notable obstacle arises from the absence of node positional information for hyperedges in hypergraphs. Therefore, different item orders can lead to identical hyperedges, which limits the formation of precise session vector representations. The paper proposes a unified framework utilizing heterogeneous hypergraph neural networks for session-based social recommendations to address these limitations. This framework utilizes hypergraphs to depict complex multivariate connections among sessions, social networks, and items. It addresses the problem of hyperedge ambiguity while maintaining the sequential order of data. The methodology entails creating a linkage graph and a session-item graph, which aid in identifying similar user intentions across various sessions and potential behavior patterns within a single session. In addition, the framework utilizes a Graph Attention Network (GAT) to combine social information from users and their connections, thereby improving the representation of user interests. Empirical assessments on three datasets show that our proposed model outperforms popular recommendation models. This emphasizes its effectiveness in accurately capturing user preferences and behaviors in session-based social recommendations.