000			02013nam a2200145 4500
008			250912b |||||||| |||| 00| 0 eng d
100			_a Aparna Kumari
245			_aUnderstanding Consumer Behavior Through AI-Powered Recommender Systems : A Systematic Review and Bibliometric Perspective
300			_app9-32
520			_aPurpose : This study examined the evolving landscape of recommender systems (RSs) and their impact on consumer purchasing behavior by identifying key research themes, trends, and gaps in the current literature. Methodology : A bibliometric analysis and systematic literature review (SLR) of 312 articles was conducted using the R package to perform performance analysis and scientific mapping, aiming to investigate the intellectual framework of the field, emerging issues, and research trends. Findings : The study identified four prominent themes: the role of RSs and AI in consumer decision support, trust, and adoption; the consumer privacy paradox in personalized commerce; and communication strategies in web personalization. The research indicated a shift toward adaptive, emotionally intelligent, and privacy-conscious RSs. Practical Implications : The results provided crucial insights for platform practitioners and developers to create RSs that enhanced personalization while addressing key issues related to trust, privacy, and user engagement, thereby improving the consumer experience and retention. Originality : This paper offered a comprehensive and organized summary of the fragmented literature on RSs and consumer behavior. By integrating bibliometric analysis with an SLR presented a unique, data-driven framework for future academic exploration and responsible innovation in digital commerce.
654			_a recommender system _ascience mapping _aconsumer behavior _aR-package _abibliometrics
700			_a Vishal Kumar Laheri
773	0		_080314 _9113840 _dIndia Associated Management Consultants _tIndian Journal of Marketing _x0973 8703
942			_cJA
999			_c132790 _d132790