Adomavicius G, Bockstedt J, Curley S, Zhang J. (2021). Effects of personalized and aggregate top-N recommendation lists on user preference ratings. Initial submission. ACM Transactions on Information Systems, 39(2), in preparation.
Adomavicius G, Bockstedt J, Curley S, Zhang J. (2021). Effects of personalized and aggregate top-N recommendation lists on user preference ratings. Initial submission. ACM Transactions on Information Systems, 39(2), in preparation.
Prior research has shown a robust effect of personalized product recommendations on user preference judgments for items. Specifically, the display of system-predicted preference ratings as item recommendations has been shown in multiple studies to bias users’ preference ratings after item consumption in the direction of the predicted rating. Top-N lists represent another common approach for presenting item recommendations in recommender systems. Through three controlled laboratory experiments, we show that top-N lists do not induce a discernible bias in user preference judgments. This result is robust, holding for both lists of personalized item recommendations and lists of items that are top-rated based on averages of aggregate user ratings. Adding numerical ratings to the list items does generate a bias, consistent with earlier studies. Thus, in contexts where preference biases are of concern to an online retailer or platform, top-N lists, without numerical predicted ratings, would be a promising format for displaying item recommendations.