Abstract
This paper addresses the problem of detecting and localizing economically significant anomalies in Digital Storefronts based on user behavior analysis. It reviews the historical development of the field through the lens of Web Mining and identifies key data structures used for modeling behavior. Particular attention is given to evaluation criteria for anomaly detection methods—such as adaptability, interpretability, automation, and sequential awareness—as well as to the limitations of existing method classes. As no single method class meets all criteria, the proposed approach relies on a modular architecture that decomposes the task into specialized subsystems. The paper outlines the conceptual architecture of such a system, integrating time series analysis (e.g., ARIMA, HMM), unsupervised learning, graph-based models (e.g., GCN), and deep learning techniques (e.g., LSTM, Transformers), with a focus on actionable and interpretable outputs for decision support.
Recommended Citation
Zhukov, V.I., "Toward Automated Detection of Economically Significant Anomalies in Digital Storefronts: Evaluation Criteria and Conceptual System Architecture" (2025). ICEB 2025 Proceedings (Hanoi, Vietnam). 14.
https://aisel.aisnet.org/iceb2025/14