Uncertainty-aware contextual multi-armed bandits for recommendations in e-commerce

Abstract

The growing e-commerce landscape has seen a shift towards personalized product recommendations, which play a critical role in influencing consumer behavior and driving revenue. This study explores the efficacy of contextual multi-armed bandits (CMAB) in optimizing personalized recommendations by intelligently balancing exploration and exploitation. Recognizing the inherent uncertainty in user behaviors, we propose an enhanced CMAB policy that incorporates item correlation matrix as an additional component of uncertainty to the conventional binary exploration and exploitation setup of bandit policies. Our approach aims to increase the overall relevance of recommendations through the 'triadic framework’ of CMAB, that seamlessly integrates with existing bandit policies, enabling adaptive recommendations based on diverse user attributes. By outperforming traditional models, this uncertainty-aware method demonstrates its potential in refining recommendation accuracy, thus maximizing revenue in a competitive e-commerce environment. Future research will explore dynamic uncertainty modeling and cross-domain applications to further advance the field.