A Self-Supervised Heterogeneous Graph Learning Framework for Customer Value Prediction

The rapid development of e-commerce systems has led to the accumulation of substantial customer data. Effectively utilizing this data for marketing and customer management is crucial for enterprises. Customer value prediction, a key task, aims to predict the revenue value of customers for the platform. Although various customer value prediction models have been proposed in existing studies, challenges remain in dealing with the complexity of heterogeneous interaction data and quantifying customer value. This is particularly because the process of data collection and obtaining customer value labels heavily relies on extensive manual annotation. To address these challenges, we propose SPHGraph, a self-supervised pre-training framework tailored for heterogeneous graphs, driven by pseudo labels automatically extracted from the data. SPHGraph integrates three core components: a pseudo label initialization module to provide structure-aware supervision, a representation learning module to capture semantic and structural information from heterogeneous interactions, and a label refinement module that iteratively improves learning signals through attention-guided propagation. This design allows the model to learn robust and transferable representations under label-scarce conditions. Extensive experiments across three real-world datasets consistently demonstrate the superior performance of SPHGraph compared to state-of-the-art baselines.

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Recommended citation: Li, A., Lin, L., Zhang, X., Xia, K., Zhang, Q., &Wang, S. (2025, December). A Self-Supervised Heterogeneous Graph Learning Framework for Customer Value Prediction. In IEEE 31th International Conference on Parallel and Distributed Systems (ICPADS). 2025.