User Needs for Explanations of Recommendations: In-depth Analyses of the Role of Item Domain and Personal Characteristics

Explanations can be provided with different goals, such as clarifying how the system works, how well the recommended item meets the user’s preferences, and how an explanation helps the user select an item faster.Although extensive research has been conducted in this research line, not much attention is paid to investigating user needs for explanations.To the best of our knowledge, no studies provide related insights, especially from the perspectives of item domain and personal characteristics.Up to now, it is not completely clear if user needs for explanations change across different item domains and vary according to user characteristics.To analyze these aspects, we developed three web-based prototype recommender systems for low-, average-, and high-involvement item domains and conducted a user study with 553 participants from different countries.Related results show that, in high-involvement item domains, users tend to have a look at explanations when they are not satisfied with the recommended items.An opposite tendency was found in low-and average-involvement item domains.Statistically, there is insufficient evidence to suggest correlations between users’ needs for explanations and item domains or between users’ needs and personal characteristics.However, the descriptive statistics show that users’ need for explanations varies across different item domains.In this study, we also found the best explanation approaches to be used in a specific recommendation domain.