A tale of two recommender systems: The moderating role of consumer expertise on artificial intelligence based product recommendations

Sydney Chinchanachokchai, Pipat Thontirawong and Punjaporn Chinchanachokchai

Journal of Retailing and Consumer Services, 2021, vol. 61, issue C

Abstract: Recommender systems are used in e-Commerce websites to make product recommendations or deliver personalized content to users. We constructed a beer recommendation program using review data from existing online community to test the hypotheses. This research aims to bridge the gap between marketing and computer science by investigating the moderating effects of consumer knowledge (expertise) on the performance and evaluation of two widely-used recommendation systems – user-based collaborative filtering and content-based. The results show that expert consumers prefer user-based collaborative filtering systems, whereas there is no difference between the two systems among novice consumers. Theoretical and managerial implications are discussed.

Keywords: Artificial intelligence; Recommender systems; Expertise; Machine learning; Algorithm; Consumer knowledge (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:joreco:v:61:y:2021:i:c:s0969698921000941

DOI: 10.1016/j.jretconser.2021.102528

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