| Title | Success Factors for Recommender Systems From a Customers' Perspective |
| Authors | Schreiner, Timo, Rese, Alexandra and Baier, Daniel |
| Year | 2020 |
| Volume | Archives of Data Science, Series A 6(2) / 2020 |
| 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. |