ScholarWorks@경상국립대학교

초록

In the context of data law compliance requirements, personalized recommendation systems have become integral to modern e-commerce platforms, yet most existing models rely solely on behavioral data and overlook the affective and cognitive dimensions of user decision-making. This limitation leads to inadequate personalization, poor generalization in cold-start scenarios, and a lack of real-time adaptability under data law frameworks. To address these challenges, the paper proposes MTL-SA, a multitask learning framework that integrates behavioral signals, sentiment-aware representations, and reinforcement learning into a unified recommendation architecture. This study demonstrates that integrating affective and behavioral feedback through multitask architectures can significantly enhance the accuracy, robustness, and human alignment of personalized recommendation systems under data legal frameworks.

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