Effectiveness of recommendation algorithms on impulsive buying in e-commerce platforms: A systematic literature review

Fadilah, Nuriya
Masarroh, Itaul
Wildan, Muhammad Alkirom

Priviet Social Sciences Journal
Vol. 5 No. 8 (2025): August 2025

This study analyzes the effectiveness of recommendation algorithms in influencing impulsive buying behavior on e-commerce platforms. Through a comprehensive review of the existing research literature, it was revealed that personalization strategies such as collaborative filtering, content-based filtering, and artificial intelligence (AI) boost impulsive buying tendencies by alleviating cognitive burdens and enhancing elements such as limited-time offers, social proof, and emotional connection. Factors such as flow experience, positive feelings, and moderating elements such as age, social media influence, and economic circumstances also play a crucial role in determining the effectiveness of these algorithms. This study provides beneficial knowledge for algorithm developers and digital marketers to refine personalization efforts and to consider psychological and contextual influences when crafting more impactful marketing strategies.