## 4 Experiments
### Baseline Methods  
To evaluate the effectiveness of the proposed model, we compare it with state-of-the-art methods.  
* Item-KNN [21] employs a nearest-neighbor approach based on cosine similarity to recommend items within a session.
* FPMC [12] leverages a combination of Markov chains and matrix decomposition for recommendation purposes.
* GRU4 Rec [13] models user interest sequences and utilizes the final state for making recommendations.  
* NARM [26] employs attention mechanisms and recurrent neural networks (RNNs) to capture users' primary interests.
* STAMP [14] incorporates the user's general intents from the current session and the most recent click.
* SASRec [30] utilizes multiple transformer layers to model user interests across session sequences.
* SR-GNN [16] leverages gated GNNs to learn complex interactions between items within session graphs.
* GC-SAN [37] employs GNNs to capture intricate item transformations and long-term dependencies within sessions while additionally incorporating self-attention networks (SANs).
* GCE-GNN [10] constructs two distinct graphs to capture local and global contextual information for recommendation purposes.
* DHCN [42] utilizes a hypergraph convolutional network to represent item-session-item relationships for recommendations.
* MTD [36] extracts intra-session and inter-session transformation relationships using a multi-tasking and multi-level network architecture.
* ICM-SR [43] retrieves representations of neighboring items while considering both a user's general and recent preferences.
* CGL [44] integrates information from the current session with global information from all sessions to generate recommendations.
* DGS-MGNN [45] incorporates a dynamic global multi-channel GNN that models items at three levels: global, local, and consensus.
* LDGC-SR [46] utilizes an adaptive weight fusion mechanism to combine long-term dependencies with global contextual information.
* GCAN [33] employs graph-enhanced attention to capture user interest in item-specific subsequences and collaborative attention to model item representations across sessions.
* CSRM [23] leverages two parallel memory modules to incorporate both current session information and collaborative neighborhood data for session-based recommendation.
* FGNN [19] employs a weighted attention graph layer to learn item embeddings within a session and utilizes a readout function to obtain a session sequence embedding representing the current session's intent.
* CTA [34] implements an attention-based sequential neural architecture that jointly models temporal and contextual information for sequential behavior modeling.
* SR-IEM [9] introduces an improved self-attention mechanism that more accurately estimates item importance, generating final recommendations by combining a user's long-term preferences with their current interests.
* HG-GNN [47] cleverly combines information about current session preferences with valuable item-transition data extracted from other users' historical sessions, leading to improved user preference inference.
* IGT [35] utilizes an interval-enhanced session graph along with a graph transformer with time intervals.