# Session-Based Recommendations for e-Commerce with Graph-Based Data Modeling
## 2 Literature Review
Deep Learning (DL) models based on Recurrent Neural Networks (RNN) have been recently proposed for session-based recommendation solutions. The work in [23] proposes the Recurrent Neural Network approach for session-based recommendations, called GRU4 REC, which employs multiple layers of the GRU model and uses only item sequences. In [24], the authors propose a hierarchical Recurrent Neural Network based again on the GRU model for session-based recommendations using user information. The work presented in [25] extends the GRU4 REC method by introducing data augmentation, and [26] proposes NARM which is an integration of a stacked GRU encoder attention mechanism to capture more representative item transition information of SBR. In [27], the authors mix the sequential patterns and co-occurrence signals by combining together the recurrent method and the neighborhood-based method to enhance the performance of the GRU4 REC recurrent model. One more DL recommendation method is based on mixture-channel purpose routing networks (MCPNs) [28]. To handle multi-purpose sessions, the authors suggest a mixture-channel model. To model the dependencies between items within each channel for a specified purpose, they create a purpose-specific recurrent network (PSRN), a variation of the GRU RNN model. The authors of [29] introduce an RNN model named Hierarchical Attentive Transaction Embedding (HATE), which exploits the attention mechanism to predict the next item by modeling dependencies in transactional data. The HATE model consists of two parts, the Inter-transaction Context Embedding part for the item representation, and the Intra-transaction Context Embedding part for the representation of multiple chosen items in the current transaction, integrating these embeddings using Intra-transaction attention. Graph Neural Network (GNN) models implement recommendation systems of various scenarios such as Social Recommendation, Sequential Recommendation, Session-based Recommendation, Bundle Recommendation, Cross-Domain Recommendation or Multi-behavior Recommendation [30]. Additionally, GNN models adopt machine learning and deep learning techniques, such as Convolutional Networks, Attention Mechanism or Embeddings representation to create recommendation systems in different domains [5]. Several next-item Graph Neural recommendation models have been proposed recently for the case of e-commerce scenarios using session-based datasets. One of Graph Neural Network's next-item recommendation approaches, named the Heterogeneous Mixed Graph Learning (HMGL) framework [31], was constructed to learn the complex local and global dependencies for next-item recommendations. HMGL encodes both session information and item attribute information into one unified graph modeling both local and global dependencies to better prepare for the next-item recommendations. In SR-GNN ([[https://github.com/CRIPAC-DIG/SR-GNN](https://github.com/CRIPAC-DIG/SR-GNN)]([https://github.com/CRIPAC-DIG/SR-GNN](https://github.com/CRIPAC-DIG/SR-GNN)), accessed on 15 March 2022) [7], the session sequences are modeled as graph-structured data. Each session is represented as the composition of the global preference and the current interest of the session. An attention network is used to learn item embeddings on the session graph, and then obtain a representative session embedding which is calculated according to the relevance of each item to the last one. The GCE-GNN ([[https://github.com/CCIIPLab/GCE-GNN](https://github.com/CCIIPLab/GCE-GNN)]([https://github.com/CCIIPLab/GCE-GNN](https://github.com/CCIIPLab/GCE-GNN)), accessed on 10 May 2022) [8] extends the previous approach by employing a session-aware attention mechanism to recursively incorporate the neighbors' embeddings of each node on the global graph. First, the session sequences are converted into session graphs to construct a global graph.