Portraying Fine-Grained Tenant Portrait for Churn Prediction Using Semi-Supervised Graph Convolution and Attention Network

With the widespread application of Big Data and intelligent information systems, the tenant has become the main form of most scenarios. As a data mining technique, the portrait has been widely used to provide targeted services. Therefore, we transfer the traditional user-driven portrait into tenant driven for churn prediction. To achieve it, this paper first proposes a three-layer architecture and defines the fine-grained features for creating portraits from the perspective of tenants. In a large-scale telecommunication industry dataset of 100,000 tenants, we construct the tenant portrait through the proposed framework, and analyze the influences of the defined features on churn possibility. Then, considering the information missing caused by privacy concerns, we come up with the CrossMatch, a portrait completion model based on semi-supervised and graph convolution, which combines the relation characteristics among tenants for recovering missing information. On this basis, we design the tenant churn prediction method based on a directed attention network. Moreover, we recover missing information on three public node datasets with CrossMatch, achieving around 1-2$\%$ improvement. We then apply the directed attention network for churn prediction and achieve an Accuracy of 75.06$\%$, Precision of 77.78$\%$, and F1-score of 71.43$\%$, which outperforms all the baselines.