Work

Cross-Session Graph and Hypergraph Co-Guided Session-Based Recommendation

Year: 2025

Type: article

Abstract: Session-based recommendation (SBR) aims to predict a user’s next item of interest by analyzing their anonymous browsing patterns. While previous studies have demonstrated considerable efficacy, they may fall short when confronted with exceedingly sparse interaction data. This paper presents a novel approach, cross-session graph and hypergraph co-guided session-based recommendation (CGH-SBR), which adeptly forecasts subsequent items while upholding efficiency and precision. First, we construct a directed graph that captures sequential dependencies by modeling cross-session item transitions, alongside building a hypergraph that encapsulates higher-order relationships between items within sessions. Subsequently, we employ two distinct graph neural networks (GNNs) to learn item representations on these two graphs separately. Further, we innovate by integrating a symmetry-aware co-guided learning framework. This framework promotes the integration of diverse perspectives and facilitates mutual learning, leveraging the data’s symmetric properties to enhance the model’s pattern recognition capabilities. Comprehensive experimentation conducted on two public datasets showcases the outstanding performance and potential of the recommendation system presented by CGH-SBR.

Source: Symmetry

DOI: 10.3390/sym17030389

Authors: Pingrong Li, Huifang Ma

Institutions: Longnan Normal University, Northwest Normal University

Language: English

Topic: Recommender Systems and Techniques

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funder: National Natural Science Foundation of China