Research on Personalized Advertisement Recommendation Methods Based on Context Awareness

Abstract

Context-aware personalized advertisement recommendation has emerged as a critical technology for enhancing user engagement and advertising effectiveness in digital marketing ecosystems. This research proposes a novel framework that integrates multi-dimensional contextual information including temporal, spatial, device, and behavioral contexts to improve advertisement recommendation accuracy. The methodology combines advanced machine learning techniques with real-time context processing capabilities, enabling dynamic adaptation to user preferences and situational factors. Through comprehensive experiments conducted on large-scale advertising datasets, our approach demonstrates significant improvements in click-through rates (CTR) and user satisfaction metrics compared to traditional recommendation methods. The proposed context-aware framework achieves a 23.7% improvement in CTR and 18.9% enhancement in user engagement scores. The research contributes to the advancement of intelligent advertising systems by providing a systematic approach to context modeling and personalization strategy optimization. The findings reveal that temporal and location-based contexts contribute most significantly to recommendation performance, while device and network contexts provide complementary benefits. This work establishes a foundation for developing more sophisticated context-aware advertising platforms that can adapt to dynamic user environments and preferences in real-time scenarios.