How can they know that? A study of factors affecting the creepiness of recommendations

Helma Torkamaan, , Catalin-Mihai Barbu, Jürgen Ziegler


Recommender systems (RS) often use implicit user preferences extracted from behavioral and contextual data, in addition to traditional rating-based preference elicitation, to increase the quality and accuracy of personalized recommendations. However, these approaches may harm user experience by causing mixed emotions, such as fear, anxiety, surprise, discomfort, or creepiness. RS should consider users’ feelings, expectations, and reactions that result from being shown personalized recommendations. This paper investigates the creepiness of recommendations using an online experiment in three domains”:” movies, hotels, and health. We define the feeling of creepiness caused by recommendations and find out that it is already known to users of RS. We further find out that the perception of creepiness varies across domains and depends on recommendation features, like causal ambiguity and accuracy. By uncovering possible consequences of creepy recommendations, we also learn that creepiness can have a negative influence on brand and platform attitudes, purchase or consumption intention, user experience, and users’ expectations of—and their trust in—RS.


Helma Torkamaan, Catalin-Mihai Barbu, and Jürgen Ziegler. 2019. How can they know that? A study of factors affecting the creepiness of recommendations. In Proceedings of the 13th ACM Conference on Recommender Systems (RecSys ’19). Association for Computing Machinery, New York, NY, USA, 423–427.