Abstract in English:

In our daily life, we need to sift through various options which often results in choice overload. Recommender systems help to overcome this problem by suggesting potentially relevant items to the users. Explaining the relevancy of these items to users has become an increasingly important goal. In recent years, a large body of research has shown that explanations are an effective means for supporting decision-making processes. However, still little is known on how to best implement these explanations and how these explanations are perceived. In addition, it is unclear how this perception is affected by the product domain or by users’ personal characteristics. To fill these research gaps, we conducted an online user study (N=291) with different design mock-ups that represent explanations of feature-based recommendations in various recommendation scenarios in two product domains (music and camera) and using different recommendation techniques (content, collaborative, and hybrid). We conducted in each domain a between-subject study with a baseline without explanations and one of the three designs explaining the feature-based recommendations. The study offers empirical evidence on how the perception of feature-based explanations in various recommendation scenarios are moderated by both the product domains and personal characteristics of the user, in particular need for cognition.