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- W3190332020 abstract "Recommender systems reduce the information overload by providing users’ “top-pick” recommendations. However, selecting the “best” out of good options can be more challenging than separating good and bad ones. Based on the of attraction effects frameworks, we examine the effectiveness of recommender systems including “worstpicks”. The addition of an unfavored item may alleviate consumers’ cognitive load and ease comparisons—all of which means the improved performance of recommendation structures. For empirical validation, we employed a randomized field experiment involving 475,339 unique users, 93,282 fashion products and 25,854,168 total instances of exposure to recommendations. The findings show that the attraction effect (AE) model outperformed the rational choice (RC) equivalent. AE model was more effective on PCs than over mobile phones. For male consumers, the AE model outcompeted the RC equivalent, but such difference was not detected among female shoppers. Based on these findings, we discuss the theoretical and practical implications." @default.
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- W3190332020 date "2019-11-01" @default.
- W3190332020 modified "2023-09-23" @default.
- W3190332020 title "A “Falsely-Framed” Choice: A Randomized Field Experiment on the Attraction Effects in Recommender Systems" @default.
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