Artificial intelligence recommender systems in online marketplaces integrating architectures consumer behavior and personalization
TL;DR
This study synthesizes the literature using a PRISMA-guided systematic review and bibliometric analysis of 135 Scopus-indexed publications published between 2007 and 2026 to provide directions for developing transparent, scalable, and consumer-centered AI personalization strategies in digital commerce.
Abstract
Artificial intelligence (AI)-based recommender systems have become a key component of personalization in online marketplaces. However, research remains fragmented across technological, behavioral, and implementation perspectives. This study synthesizes the literature using a PRISMA-guided systematic review and bibliometric analysis of 135 Scopus-indexed publications published between 2007 and 2026. The findings reveal rapid growth in scholarly interest, particularly after 2020, driven by the increasing adoption of AI-driven personalization. Methodologically, the field is dominated by machine learning, collaborative filtering, deep learning, and hybrid recommender approaches. Six thematic clusters were identified, covering recommendation techniques, consumer behavior, predictive analytics, user experience, platform environments, and system integration. The review also highlights a persistent gap between experimental model performance and scalable marketplace deployment. By integrating technological, behavioral, and infrastructural perspectives, this study provides directions for developing transparent, scalable, and consumer-centered AI personalization strategies in digital commerce.