Disentangled Cascaded Graph Convolution Networks for Multi-Behavior Recommendation

TL;DR

This paper introduces Disen-CGCN, a novel multi-behavior recommendation model that disentangles user and item factors, personalizes feature transformations, and employs attention mechanisms to improve recommendation accuracy by leveraging multi-behavioral data.

Contribution

Disen-CGCN is the first model to combine disentangled representations, personalized multi-behavioral transformations, and attention mechanisms for enhanced multi-behavior recommendation.

Findings

Outperforms state-of-the-art models by 7.07% and 9.00% on benchmark datasets.

Effectively captures user preferences across multiple behaviors.

Improves recommendation accuracy through disentangled and personalized modeling.

Abstract

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.