  Multi-behavioral recommender systems have emerged as a solution to address
data sparsity and cold-start issues by incorporating auxiliary behaviors
alongside target behaviors. However, existing models struggle to accurately
capture varying user preferences across different behaviors and fail to account
for diverse item preferences within behaviors. Various user preference factors
(such as price or quality) entangled in the behavior may lead to
sub-optimization problems. Furthermore, these models overlook the personalized
nature of user behavioral preferences by employing uniform transformation
networks for all users and items. To tackle these challenges, we propose the
Disentangled Cascaded Graph Convolutional Network (Disen-CGCN), a novel
multi-behavior recommendation model. Disen-CGCN employs disentangled
representation techniques to effectively separate factors within user and item
representations, ensuring their independence. In addition, it incorporates a
multi-behavioral meta-network, enabling personalized feature transformation
across user and item behaviors. Furthermore, an attention mechanism captures
user preferences for different item factors within each behavior. By leveraging
attention weights, we aggregate user and item embeddings separately for each
behavior, computing preference scores that predict overall user preferences for
items. Our evaluation on benchmark datasets demonstrates the superiority of
Disen-CGCN over state-of-the-art models, showcasing an average performance
improvement of 7.07% and 9.00% on respective datasets. These results highlight
Disen-CGCN's ability to effectively leverage multi-behavioral data, leading to
more accurate recommendations.
