Schreiner, Timo, Rese, Alexandra and Baier, Daniel - Archives of Data Science, Series A

Article Details

Title Success Factors for Recommender Systems From a Customers' Perspective
Authors Schreiner, Timo, Rese, Alexandra and Baier, Daniel
Year 2020
Volume 6(2)
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 recom- mendations 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 dif- ferent 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 rel- evance with banner advertising being the least preferred channel. Moreover, differences between male and female respondents are outlined. Finally, im- plications for retailers and advertisers are discussed and a brief outlook on future developments is presented.