Editorials in Proceedings:
P. Knees, K. Andersen, A. Said, M. Tkalcic:
"UMAP 2017 Workshop on Surprise, Opposition, and Obstruction in Adaptive and Personalized Systems";
in: "Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization",
New York, NY, USA,
It is our great pleasure to welcome you to the UMAP 2017 Workshop on Surprise, Opposition, and Obstruction in Adaptive and Personalized Systems (SOAP). Following the successful first edition of the workshop at UMAP 2016, we are happy to see a continuing and increasing interest in the workshop's topics. As with the first edition, for the second edition we were able to accept four highly relevant submissions, allowing us to discuss the challenges of recommending unexpected, nonetheless relevant and impactful artifacts during a focused half-day workshop. With the workshop being originally motivated by interviews with music creators and producers who articulated a strong rejection of "more-of-the-same" search engines and recommender systems as they challenge their notion of originality and, ultimately, pose a threat to their artistic identity, we realized that a demand for adaptive and personalized systems that not only have the capability to surprise, but also to oppose and even obstruct can be found in a wider field. In fact, this coincides with ongoing trends to deal with and escape generally negatively connoted effects of automatic recommender systems, such as the so-called "filter-bubble". Apart from the potential dangers of such effects on the unreflecting user, there seems to be a growing impression that collaborative, as well as content-based recommender systems keep making obvious, uninspiring, and therefore disengaging suggestions based on previous interactions. Over the last years, this has emphasized the value of system qualities beyond pure accuracy, e.g., diversity, novelty, serendipity, or unexpectedness, to keep the user satisfied. In fact, these approaches to kicking the user out of his or her "comfort zone" seem to be highly promising methods to increase satisfaction with a system in the long run.
"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)
Electronic version of the publication:
Created from the Publication Database of the Vienna University of Technology.