An Intelligent System for Ranking E-commerce Customer Reviews to Boost Engagement

This study introduces an innovative framework that utilizes learning algorithms to rank customer reviews on e-commerce platforms. Addressing the ambiguity and subjectivity in customer feedback, our approach highlights the use of an extensive dataset and feature engineering. A pivotal part of our methodology is the creation of an original target variable named ‘adjusted action rate’ (AAR), combined with advanced training techniques to alleviate ‘position bias’. This strategy allows us to effectively capture the nuances of user behavior and review dynamics. At the core of our framework are Learning to Rank (LTR) methods, specifically designed to tackle the unique challenges of review ranking. Our primary evaluation criterion is the Normalized Discounted Cumulative Gain (nDCG) metric, which assesses the efficiency of our LTR algorithm in predicting purchase likelihood based on user reviews. Validation through online A/B testing shows that our framework significantly improves user interaction, decisionmaking efficiency, and the overall shopping experience on e-commerce sites. The results confirm the success of our strategy in overcoming the complexities of review ranking, evidenced by notable enhancements in engagement metrics.