A Dynamic Session-Based Recommendation System with Graph Neural Networks

Session-based recommendation systems provide personalized recommendations to users based on their session activities. Traditional recommendation algorithms often overlook temporal dependencies within user sessions, leading to suboptimal recommendations. To address this limitation, we proposed a Temporal Graph Neural Network (TemporalGNN) approach that leverages the temporal relationships between items in sessions to enhance recommendations. The proposed session-based recommendation system can effectively capture temporal dependencies within user sessions. The method consists of two temporal GNN layers designed to capture temporal dependencies within user sessions. A directed graph is constructed from session data, where each unique item in the dataset is a node, and directed edges are created between consecutive items within a session, with edge weights representing the time differences between interactions. This graph structure allows the model to capture the sequential nature of user interactions. The model generates recommendations by computing similarity scores from the learned embeddings and selecting the top N items with the highest scores. The experimental results on two real-world datasets showed the effectiveness of the proposed method in improving recommendation performance compared to the baseline approaches. The source code implementation is available on the GitHub repository at https://github.com/VrushaliM/SB-Recommendation-GNN.