Dynamic hypergraph for cross-domain session-based social recommendations

The dynamic and temporal nature of user interactions on social media platforms has increased the importance of session-based recommendations. The adoption of graph neural networks in these systems has increased due to their capacity to incorporate structural topology and node information. Nevertheless, current session-based recommendation methods relying on pairwise graph structure ignore intricate and evolving interactions between users, items, and sessions across domains. However, most methodologies focus on a scenario where consumers interact with a single domain. As a result, they cannot accurately represent the intricate and evolving correlations between users and items in cross-domain scenarios. They consistently face insufficient data because they overlook the fact that users’ behaviors are distributed across domains and sessions. To address these challenges, we propose a novel dynamic hypergraph-based framework for cross-domain session-based social recommendations (DHCSRs). We construct a dynamic heterogeneous hypergraph to model the intricate higher order correlations among inter- and intradomain sessions, items, users, and social networks. In intradomain sessions, the hyperedge incorporates domain-specific features into the embedding process for user embedding by considering product categories. Interdomain sessions capture the complex interests of users who engage with diverse types of content. In addition, the framework utilizes gated recurrent units for user behavior and a domain-driven session-based RNN to capture user and session representations. The domain-driven session-based RNN exchanges information across sessions from various domains in sequential order to ensure alignment of domain interactions. The user behavior RNN learns the user’s global interests and captures behavioral differences across domains. Finally, user behavioral information and session representations are aggregated with the session and item representations from the hypergraph to predict user behavior. Comprehensive experiments demonstrate that the DHCSR model outperforms several baselines across all four datasets, with improvements ranging from 3.56% to 18.31% in HR and from 5.08% to 25.65% in NDCG, significantly enhancing the accuracy of recommendations.