Self-Attention Residual Connection and Graph Neural Hawkes Bilayer Model for Session-Based Recommendation

Session-based recommendation aims to make recommendations for anonymous users based on limited session data. However, traditional session-based recommendation methods fail to capture complex item transitions and simply represent the user's last clicked item as a short-term preference, neglecting the global sequential information of the session. This approach struggles to consider transitions between contexts and cannot accurately capture the user's true intentions. To address these issues, this paper proposes a session recommendation method based on self-attention residual connections and graph neural Hawkes (SRGNH). This method introduces a duallayer network structure consisting of graph neural self-attention residual connection layers and graph neural Hawkes layers, designed to learn users' long-term and short-term preferences, respectively. SRGNH employs a Gated Graph Neural Network (GGNN) to capture complex interactions between nodes, obtaining latent vectors for each item. It incorporates self-attention networks and residual connections to effectively utilize low-level inspired information for capturing users' long-term preferences. The graph neural Hawkes layer combines the Hawkes process with GGNN to capture the relationship between user item clicks over continuous time, accurately representing users' short-term preferences. To better represent user intent, we linearly combine users' long-term and short-term preferences in the end. Experimental results demonstrate that the proposed SRGNH outperforms other recommendation models on the Diginetica, Yoochoose1/64, and Yoochoose1/4 datasets.