Self-supervised session-based recommendation model based on global causal structure learning

Key Points

• Enhanced recommendations resulted from a self-supervised algorithm based on causal structure learning.
• The model captures relationships in user behavior to improve recommendation accuracy, showing significant improvement in user satisfaction.
• Utilizing causal structure learning allows for better prediction in session-based recommendation systems compared to traditional methods.
• Incorporating this approach may enable more effective recommendations, but further validation in diverse settings is needed.