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Success Factors for Recommender Systems From a Customers’ Perspective

Schreiner, Timo; Rese, Alexandra; Baier, Daniel

Abstract:
Recommender systems have become an integral part of today’s ecommerce landscape and are no longer only deployed on websites but also increasingly serve as a basis for the delivery of personalized product recommendations in various communication channels. Within this paper, we present a brief overview of popular and commonly used recommender algorithms as well as current cutting-edge algorithmic advances. We examine consumers’ preferences regarding product recommendations in advertisements across different media channels within the apparel industry by applying choice-based conjoint analysis. The findings of studies for young male (n = 170) and female (n = 162) consumers show that the recommender algorithm is not necessarily of upmost importance. In contrast, the advertising channel is of highest relevance with banner advertising being the least preferred channel. Moreover, differences between male and female respondents are outlined. Finally, implications for retailers and advertisers are discussed and a brief outlook on future developments is presented.

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Verlagsausgabe §
DOI: 10.5445/KSP/1000098012/02
Veröffentlicht am 12.10.2020
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Wirtschaftsinformatik und Marketing (IISM)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2020
Sprache Englisch
Identifikator ISSN: 2363-9881
KITopen-ID: 1000124504
Erschienen in Archives of Data Science, Series A
Band 6
Heft 2
Seiten P02, 20 S. online
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