{"title":"A Theory of Recommendations","authors":"Jean-Michel Benkert, Armin Schmutzler","doi":"arxiv-2408.11362","DOIUrl":null,"url":null,"abstract":"This paper investigates the value of recommendations for disseminating\neconomic information, with a focus on frictions resulting from preference\nheterogeneity. We consider Bayesian expected-payoff maximizers who receive\nnon-strategic recommendations by other consumers. The paper provides conditions\nunder which different consumer types accept these recommendations. Moreover, we\nassess the overall value of a recommendation system and the determinants of\nthat value. Our analysis highlights the importance of disentangling objective\ninformation from subjective preferences when designing value-maximizing\nrecommendation systems.","PeriodicalId":501273,"journal":{"name":"arXiv - ECON - General Economics","volume":"33 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - ECON - General Economics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2408.11362","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This paper investigates the value of recommendations for disseminating
economic information, with a focus on frictions resulting from preference
heterogeneity. We consider Bayesian expected-payoff maximizers who receive
non-strategic recommendations by other consumers. The paper provides conditions
under which different consumer types accept these recommendations. Moreover, we
assess the overall value of a recommendation system and the determinants of
that value. Our analysis highlights the importance of disentangling objective
information from subjective preferences when designing value-maximizing
recommendation systems.