Self-supervised progressive graph neural network for enhanced multi-behavior recommendation

Abstract

Multi-behavior recommendation (MBR) aims to enhance the accuracy of predicting target behavior by considering multiple behaviors simultaneously. Recent researches have attempted to capture the dependencies within behavioral sequences to improve recommendation outcomes, exemplified by the sequential pattern “click\(\rightarrow \)cart\(\rightarrow \)buy”. However, their performances are still limited due to the following two problems. Firstly, potential leapfrogging relations among behaviors are underexplored, notably in cases where users purchase directly post-click, bypassing the cart stage. Skipping intermediate behavior allows for better modeling of real-world realities. Secondly, the uneven distribution of user behaviors and item popularity presents a challenge for model training, resulting in prevalence bias and over-reliance issues. To this end, we propose a self-supervised progressive graph neural network model, namely SSPGNN. The model can capture a broader range of behavioral dependencies by using a dual-behavior chain. In addition, we design a self-supervised learning mechanism, including intra- and inter-behavioral self-supervised learning, the former within a single behavior and the latter across multiple behaviors, to address the problems of prevalence bias and overdependence. Extensive experiments on real-world datasets and comparative analyses with state-of-the-art algorithms demonstrate the effectiveness of the proposed SSPGNN. The source codes of this work are available at https://github.com/ZZY-GraphMiningLab/SSPGNN.