Multi Behavior

Multi-behavior research focuses on understanding and leveraging the diverse ways users interact with systems, particularly in recommendation systems and online platforms. Current research emphasizes developing models that effectively capture the complex relationships between different user behaviors (e.g., clicks, purchases, views), often employing graph neural networks, transformers, and contrastive learning techniques to improve recommendation accuracy and address data sparsity and imbalance issues. This work is significant because accurately modeling multi-behavior data leads to more personalized and effective systems, impacting areas like e-commerce, job recruitment, and social media.