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Cognitive Computing-Enabled Personalized Consumer Electronics Recommendations Using Economic Policy Uncertainty
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DOI：10.1109/MCE.2024.3416903.png)
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摘要
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This article explores the fusion of economic policy uncertainty (EPU) metrics with cognitive computing to enhance personalized consumer electronics products and user recommendations. Leveraging advanced AI techniques, i.e., large Language models (LLMs), we aim to dynamically adapt recommendation systems to changing economic conditions to enhance user experience. Challenges such as uncertain economic policies and complex feature engineering are addressed with innovative methodologies. We investigate the potential of EPU-driven cognitive computing in improving consumer decision-making and suggest future research directions, including the integration of domain-specific knowledge. By bridging economic uncertainty, cognitive computing paradigm (i.e., LLMs), and personalized recommendations in the consumer electronics domain, this research contributes to theoretical understanding and practical applications, aiming to refine recommendation systems amid economic uncertainty.
Keyword:
Consumer electronics
Economics
Recommender systems
Cognitive systems
Uncertainty
Training
Computational modeling
期刊
IF:
4.1
论文数:
1.3K
被引数:
1.8K
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