2019

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

Helma Torkamaan, , Catalin-Mihai Barbu, Jürgen Ziegler Abstract 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, …

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Rating-Based Preference Elicitation for Recommendation of Stress Intervention

Helma Torkamaan, Jürgen Ziegler Abstract In recent years, recommender systems have emerged as a key component for personalization in health applications. Central in the development of recommender systems is rating-based preference elicitation, based both on single-criterion and multi-criteria rating. Though its use has already been studied in various domains of recommender systems, far too little …

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