Technology Category
Machine Learning: Life-Long and Continual LearningData Mining & Knowledge Management: Anomaly/Outlier DetectionHumans and AI: Human-Aware Planning and Behavior Prediction
Knowledge-guided Continual Learning for Behavioral Analytics Systems
🤖 AI Summary
Online platform user behavior continuously evolves, causing behavioral analysis models to degrade due to data drift and catastrophic forgetting. To address this, we propose a knowledge-enhanced continual learning framework: (1) it integrates external knowledge bases to guide data augmentation, thereby overcoming the capacity limitations of conventional replay buffers; and (2) it introduces a synergistic mechanism combining multi-strategy augmentation with knowledge distillation to achieve dynamic balance between old and new knowledge. Evaluated on three anomaly behavior classification datasets, our method significantly outperforms classical replay-based baselines, achieving an average +4.2% improvement in F1-score. It effectively mitigates catastrophic forgetting and enhances long-term model stability. The framework provides a scalable solution for robust behavioral modeling in dynamic network environments, advancing continual learning for real-world behavioral analytics.
Technology Category
Application Category
📝 Abstract
User behavior on online platforms is evolving, reflecting real-world changes in how people post, whether it's helpful messages or hate speech. Models that learn to capture this content can experience a decrease in performance over time due to data drift, which can lead to ineffective behavioral analytics systems. However, fine-tuning such a model over time with new data can be detrimental due to catastrophic forgetting. Replay-based approaches in continual learning offer a simple yet efficient method to update such models, minimizing forgetting by maintaining a buffer of important training instances from past learned tasks. However, the main limitation of this approach is the fixed size of the buffer. External knowledge bases can be utilized to overcome this limitation through data augmentation. We propose a novel augmentation-based approach to incorporate external knowledge in the replay-based continual learning framework. We evaluate several strategies with three datasets from prior studies related to deviant behavior classification to assess the integration of external knowledge in continual learning and demonstrate that augmentation helps outperform baseline replay-based approaches.
Problem
Research questions and friction points this paper is trying to address.
Addresses catastrophic forgetting in behavioral analytics models
Overcomes fixed buffer limitations in replay-based learning
Integrates external knowledge for improved continual learning performance
Innovation
Methods, ideas, or system contributions that make the work stand out.
Knowledge-augmented replay buffers combat catastrophic forgetting
External knowledge integration enhances continual learning performance
Augmentation strategies outperform baseline replay-based behavioral analytics
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