 Economic corollaries of personalized recommendationsMir Majid Molaie and Wonjae Lee Journal of Retailing and Consumer Services, 2022, vol. 68, issue C Abstract: The impact of recommendation systems (RSs) on the diversity of consumption is not transparent or well understood. Available studies, whether experimental or theoretical, show inconsistent and even opposite results, which manifests as debate in the literature. In this paper, we investigate the impact of two main recommender systems, neural collaborative filtering and deep content filtering, on sales diversity via a randomized field experiment. Our results confirm the capability of recommender engines in increasing or decreasing aggregate sales diversity. Nonetheless, they amplify homogenization and reduce individual-level consumption diversity. In conclusion, our research reconciles seemingly contradict previous findings and illustrates that the design of the RS is the decisive factor in homogenizing or diversifying product sales. Keywords: Artificial intelligence; Consumer purchase diversity; E-commerce; Field experiment; Machine learning; Neural networks; Personalization; Recommender systems; Sale diversity (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:joreco:v:68:y:2022:i:c:s0969698922000960 DOI: 10.1016/j.jretconser.2022.103003 Access Statistics for this article Journal of Retailing and Consumer Services is currently edited by Harry Timmermans More articles in Journal of Retailing and Consumer Services from Elsevier |
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