Exploring Users' Perception of Collaborative Explanation Styles
This addresses the problem of improving recommendation system explainability for users, though it appears incremental as it builds on existing user-based and item-based paradigms.
The study investigated how users perceive different collaborative explanation styles in recommendation systems, finding that the mean rating value has a higher impact on user decisions than the total number of ratings.
Collaborative filtering systems heavily depend on user feedback expressed in product ratings to select and rank items to recommend. In this study we explore how users value different collaborative explanation styles following the user-based or item-based paradigm. Furthermore, we explore how the characteristics of these rating summarizations, like the total number of ratings and the mean rating value, influence the decisions of online users. Results, based on a choice-based conjoint experimental design, show that the mean indicator has a higher impact compared to the total number of ratings. Finally, we discuss how these empirical results can serve as an input to developing algorithms that foster items with a, consequently, higher probability of choice based on their rating summarizations or their explainability due to these ratings when ranking recommendations.