Workshop on surprise, opposition, and obstruction in adaptive and personalized

P. Knees, K. Andersen, Alan Said, M. Tkalčič. 2016, "Workshop on surprise, opposition, and obstruction in adaptive and personalized". Late-breaking Results, Posters, Demos, Doctoral Consortium and Workshops Proceedings of the 24th ACM Conference on User Modeling, Adaptation and Personalisation (UMAP 2016).

Abstract

The phenomenon often referred to as the “filter-bubble,” i.e., the effect that collaborative, as well as content-based recommender systems keep making obvious, predictable, redundant, uninspiring, and therefore disengaging suggestions based on previous interactions, has emphasized the value of system qualities beyond pure accuracy, e.g., diversity, novelty, serendipity, or unexpectedness, to keep the user satisfied, e.g., [1, 2, 4]. Apart from the obvious use case in commercial systems (where this user satisfaction directly translates to revenue), these additional qualities become even more important in other areas. For instance, in creative domains, such as music production, we find that similaritybased, “more of the same” recommendations have basically no relevance, as illustrated by a quote from a professional music producer on the use of recommender systems that could predict his behavior in the process of music making: “I would be more interested in something that made me sound like the opposite of me […] cause I can’t do that on my own” (anonymous, during interview on location at the Red Bull Music Academy 2014, cf. [3]).