Enhancing e-commerce personalization with review-based adaptive feature matching: a real-time approach

Abstract

The widespread evolution of e-commerce platforms necessitates advanced personalization techniques to enhance user experience and satisfaction. Our paper introduces the review-based adaptive feature matching (R-AFM) algorithm, an innovative approach to real-time personalization in e-commerce settings. Leveraging the rich data from user reviews and product metadata available in the Amazon product review dataset, R-AFM dynamically adapts to user preferences and behaviors through a sophisticated feature matching process. The methodology encompasses data collection, feature extraction, user preference modeling, real-time recommendation generation, and an adaptive feedback loop. By analyzing historical review data alongside real-time user interactions, R-AFM updates preference weights for product features, thereby refining the personalization mechanism. This process culminates in the generation of highly personalized product recommendations. Comparative analysis with existing personalization methods-collaborative filtering (CF), content-based filtering (CBF), hybrid recommender systems (Hybrid RS), and deep learning-based recommender systems (DL-RS)-demonstrates R-AFM's superior performance improvement varying between 2 to 8% in terms of accuracy, precision, recall, and F1-score. The algorithm's unique capability to incorporate real-time feedback significantly enhances the e-commerce personalization landscape, offering promising avenues for future research and practical application.