ADPal: Automatic Detection of Troubled Users in Online Service Systems via Page Access Logs

Online service providers rely on customer service to enhance the experience of troubled users who encounter problems when interacting with online services. Nowadays, the customer service usually follows a reactive style, i.e., users with problems actively resort to help, and the customer service passively solves problems. But a more ideal style pursued by online service providers is proactive customer service, where users with problems can be detected in advance and notified of possible solutions before they resort to help. However, is it possible to detect users with problems? To answer the question, in this paper, we collect user traces of page access logs and problem feedback through customer service from a commercial online service provider Fliggy. We first verify an intuition that the page access logs are a good indicator to detect users with problems. Based on this verification, we design ADPal, an approach to detecting users with problems. Given a user’s page access log, ADPal outputs whether he/she encounters problems. ADPal leverages the capability of Transformer to extract relationships between pages, achieving high effectiveness and high efficiency. Evaluations on real-world data sets show that ADPal can achieve P@1000 74.70%, outperforming state-of-the-art anomaly detection approaches.