Input or Output: Effects of Explanation Focus on the Perception of Explainable Recommendation with Varying Level of Details

In this paper, we shed light on two important design choices in explainable recommender systems (RS) namely, explanation focus and explanation level of detail. We developed a transparent Recommendation and Interest Modeling Application (RIMA) that provides on-demand personalized explanations of the input (user model) and output (recommendations), with three levels of detail (basic, intermediate, advanced) to meet the demands of different types of end-users. We conducted a within-subject study to investigate the relationship between explanation focus and the explanation level of detail, and the effects of these two variables on the perception of the explainable RS with regard to different explanation aims. Our results show that the perception of explainable RS with different levels of detail is affected to different degrees by the explanation focus. Consequently, we provided some suggestions to support the effective design of explanations in RS.

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