PIOF : A Fairness‐Aware Deep Reinforcement Learning Framework for Personalized Recommendations in E‐Commerce Platforms

ABSTRACT

The fast expansion of artificial intelligence (AI) technologies across the ecommerce ecosystems has radically changed the processes of how firms interact with consumers. The paper explores the complex effect of AI‐based personalization on consumer decision making and the firm‐level performance outcomes, based on e‐commerce sites. Our proposed Personalization Impact Optimization Framework (PIOF) represents a blend of collaborative filtering, deep neural networks, and reinforcement learning as a framework to model the user preference dynamics in real‐time. The mathematical model integrates a multi‐objective optimization criterion taking into consideration both consumer utility and revenue of the firm. We show that conversion rates increase with AI‐based engines of personalization by a maximum of 34.7%, consumer search costs with AI‐based personalization engines decrease by 28.3% and the average value of orders with AI‐based personalization engines by 19.6%, compared to controls without personalization. Moreover, our econometric model shows that there is a statistically significant positive correlation between the extent of personalization and the amount of gross merchandise volume (GMV) by the firm. The combination of reinforcement learning and fairness‐aware multi‐objective optimization led to better performance than sequential recommenders based on transformers. Such results have significant implications on platform strategy, algorithmic governance and the consumer welfare policy.