A Novel session-based recommendation system using capsule graph neural network

Session-based recommendation systems (SBRS) are essential for enhancing the customer experience, improving sales and loyalty, and providing the possibility to discover products in dynamic and real-world scenarios without needing user history. Despite their importance, traditional or even current SBRS algorithms face limitations, notably the inability to capture complex item transitions within each session and the disregard for general patterns that can be derived from multiple sessions. This paper proposes a novel SBRS model, called Capsule GraphSAGE for Session-Based Recommendation (CapsGSR), that marries GraphSAGE's scalability and inductive learning capabilities with the Capsules network's abstraction levels by generating multiple integrations for each node from different perspectives. Consequently, CapsGSR addresses challenges that may hinder the optimal item representations and captures transitions' complex nature, mitigating the loss of crucial information. Our system significantly outperforms baseline models on benchmark datasets, with improvements of 8.44% in HR@20 and 4.66% in MRR@20 , indicating its effectiveness in delivering precise and relevant recommendations.