Introducing Surprise and Opposition by Design in Recommender Systems

Abstract

There is a long tradition in recommender systems research to evaluate systems using quantitative performance measures on fixed datasets. As a reaction to this narrow accuracy-based focus in research, novel qualities beyond pure accuracy are emphasized in recent research; among them are surprise and opposition. This position paper considers that the perception of surprise and/or opposition may be purposely prepared when several recommendations are provided (e.g., in terms of a music playlist) or the user is given the choice between several options. Altering users’ perception and triggering according behavior is well rooted in research on priming from psychology and nudge theory from the field of economic behavior. In this position paper, we propose how priming and nudging may be integrated into the design and evaluation of recommender systems to arouse surprise and opposition.

Publication
2nd Workshop on Surprise, Opposition, and Obstruction in Adaptive and Personalized Systems, in conjunction with 25th International Conference on User Modeling, Adaptation and Personalization (UMAP 2017)