## 2 Related Work
### GNN-Based Methods  
Since graph neural networks (GNNs) show high performance in areas such as knowledge graph extraction and next location prediction, a large number of GNN-based recommendation system approaches have been developed recently [38; 39]. SR-GNN is the first approach to use GNNs in session-based recommendations [16]. SGNN-HN model uses a Star Graph Neural Network (SGNN) to capture the complex transition relationships between items within an active user session. To prevent overfitting, Highway Networks (HNs) are utilized to dynamically select the most relevant embeddings from the item representations [40]. FGNN employs weighted graph-based attention networks to determine relationships between items by structuring sessions as weighted graphs [19]. LESSR evaluates the information loss in the graph generation process and proposes a lossless information encoding method [41]. DHCN designs a hypergraph-based convolutional network to improve recommendation tasks [42].  
ICM-SR uses representation vectors of neighboring items to model the user's latent intent and applies a session representation encoder to consider the user's global and recent preferences [43]. CGL builds a global graph from all sessions and adopts a self-supervised learning strategy to train the recommendation model using information from current and past sessions [44]. DGS-MGNN proposes a dynamic global multi-channel GNN approach that models the global, local, and joint representation of items [45]. LDGC-SR uses an adaptive weight fusion mechanism to combine the user's long-term trends with global contextual information [46]. GCE-GNN includes collaborative information by incorporating similar sessions or items, which may harm personalized modeling [10]. HG-GNN formulates a heterogeneous global graph neural network architecture that integrates present session preferences with informative item transitions derived from historical sessions of other users, enhancing the inference of user preferences [47].  
A session-based recommendation system is characterized in terms of the capture of session-sequential features, the loss function, and the properties of the architecture involved. The characterisation of the related methods is shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models** & **Session-Sequential** & **Loss Function** & **Methodology** \\ \hline Item-KNN [21] & Session & BPR & KNN \\ FPMC [12] & Session & S-BPR & LFM+Markov Chains \\ GRU4 Rec [13] & Session & BPR, TOP1 & GRU \\ NARM [26] & Sequential & BCE & Attention \\ STAMP [14] & Session & BCE & Attention \\ SASRec [30] & Sequential & BCE & Transformer \\ SR-GNN [16] & Session & BCE & GNN \\ GC-SAN [37] & Sequential & BCE & GNN+Attention \\ GCE-GNN [10] & Session & BCE & GNN+Attention \\ DHCN [42] & Session & BCE & Graph Convolutional Network \\ MTD [36] & Sequential & BCE & Graph+Attention \\ ICM-SR [43] & Session & BCE & Graph+Collaborative \\ GGL [44] & Session & KLD & Graph+Collaborative \\ DGS-MGNN [45] & Session & BCE & GNN+Attention \\ LDGC-SR [46] & Session & BCE & Graph+Attention \\ GCAN [33] & Sequential & BCE & Graph+Collaborative \\ CSRM [23] & Session & BCE & Encoder+RNN \\ FGNN [19] & Session & BCE & GNN \\ CTA [34] & Sequential & NLL, BPR & Attention \\ SR-IEM [9] & Session & BCE & Attention \\ HG-GNN [47] & Sequential & BCE & GNN+Encoder \\ IGT [35] & Sequential & BCE & Graph+Transformer \\ SkipGT [Ours] & Both & BCE & Skip-gram+Transformer \\ \hline \hline \end{tabular}
\end{table}
Table 1: Technical comparison between related methods and our method.