CCT-GNN: Collaborative category and time-aware graph neural networks for session-based recommendation systems

Abstract

A session-based recommendation system (SBRS) focuses on the user’s interactions in the current session to provide recommendations. Recent works employ graph neural networks (GNN) to capture complex relationships between clicked items in a session. Some of these studies incorporate collaborative information from similar/neighbor sessions. To identify neighbor sessions, they calculate the similarity of sessions by counting the common items between two sessions. But this is too simplistic and the similarity depends on other features such as the order of common items. Furthermore, meta-data such as items’ categories have not been considered, while items can be categorized into a limited number of categories, which can be informative in finding similar sessions and predicting user intent. In this paper, we propose a novel method, named Collaborative Category and Time-aware Graph Neural Networks (CCT-GNN), which models users’ interactions in two levels: (i) Global-level, which identifies neighbor sessions effectively and explores collaborative information to improve model performance. (ii) Local-level, in which the current session interactions are transformed into an item category graph to model different types of relations between items and categories. Experimental results demonstrate CCT-GNN superiority over state-of-the-art methods. Source code is available at: https://github.com/Moosazadeh/CCT-GNN. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.