A hybrid approach to behavioral spam review detection on e-commerce platforms using apriori and CNN

Abstract

Spam reviews significantly undermine the credibility of online review systems on e-commerce websites. This paper presents a hybrid methodology that combines the Apriori algorithm and convolutional neural networks (CNN) to efficiently identify and mitigate spam reviews. By examining user behavior, including activity patterns, reviewer reputation, temporal dynamics, and sentiment consistency, we propose a comprehensive model for understanding user interactions and engagement. To extract important information and build precise spam detection models, we use data mining and machine learning approaches. Furthermore, contextual and domain-specific analyses are conducted to improve detection strategies. The study highlights the significance of hybrid techniques in preserving the integrity of e-commerce platforms through successful industry implementations and presents evaluation metrics, problems, and future research objectives.