Rating-based summary statistics are ubiquitous in e-commerce, and often are crucial components in personalized recommendation mechanisms. Especially visual rating summarizations have been identiied as important means to explain, why an item is presented or proposed to an user. Largely left unexplored, however, is the issue to what extent the descriptives of these rating summary statistics inluence decision making of the online consumer. Therefore, we conducted a series of two conjoint experiments to explore how diferent summarizations of rating distributions (i.e., in the form of number of ratings, mean, variance, skewness, bimodality, or origin of the ratings) impact users' decision making. In a irst study with over 200 participants, we identiied that users are primarily guided by the mean and the number of ratings, and - to lesser degree - by the variance and origin of a rating. When probing the maximizing behavioral tendencies of our participants, other sensitivities regarding the summary of rating distributions became apparent. We thus instrumented a follow-up eye-tracking study to explore in more detail, how the choices of participants vary in terms of their decision making strategies. This second round with over 40 additional participants supported our hypothesis that users, who usually experience higher decision diiculty, follow compensatory decision strategies, and focus more on the decisions they make. We conclude by outlining how the results of these studies can guide algorithm development, and counterbalance presumable biases in implicit user feedback.

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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.

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Collaborative filtering systems heavily depend on user feedback expressed in product ratings to select and rank items to recommend. These summary statistics of rating values carry two important descriptors about the assessed items, namely the total number of ratings and the mean rating value. In this study we explore how these two signals influence the decisions of online users based on choice-based conjoint experiments. Results show that users are more inclined to follow the mean indicator as opposed to the total number of ratings. 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 it explainability due to these ratings when ranking recommendations.@en