Abstract
The volume of daily email traffic continues to grow rapidly, creating challenges in efficiently distinguishing important from irrelevant messages. Beyond spam detection, modern email systems classify messages into categories such as promotions, social, updates, and forums, many of which are ignored or deleted without review. To address this issue, researchers have explored intelligent classification systems to predict the importance of emails, enhance user productivity, and improve organizational communication efficiency. This study proposes an email classification model that adapts to different users’ work functions and communication patterns within an organizational context. Using three-month historical real corporate anonymized email data from 9788 individuals across 12 work functions, the proposed Adaptive Semi-Personalized Email Classification Model (ASPEC) automatically retrieves each employee’s occupational profile—including job category and years of work experience—from the organization’s Human Resources (HR) system, enabling seamless personalization without manual configuration. ASPEC significantly improves email classification accuracy over the best-performing baseline of 73.50%, with incremental learning further enabling continuous adaptation to evolving data streams and achieving accuracy up to 92.57% in stable user segments. Unlike most existing email classification frameworks, which rely on static batch-learning models and lack memory-based or incremental update mechanisms, ASPEC addresses this gap by continuously adapting to evolving communication patterns without requiring full model retraining. The adoption of this incremental learning framework offers tangible benefits for organizations, including reduced manual email filtering workload, improved communication efficiency, and decreased operational burden on IT departments in managing email-related tasks and issues.