Alexander Felfernig in conjunction with 13th ACM Conference on Recommender Systems (RecSys 2019)
edited by
Copyright © 2019 for the individual papers by the papers' authors. Copyright © 2019 for the volume as a collection by its editors. This volume and its papers are published under the Creative Commons License Attribution 4.0 International (CC BY 4.0).
Preface This volume contains the papers presented at the 6th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS), held as part of the 13th ACM Conference on Recommender System (RecSys), in Copenhagen, Denmark.
RecSys is the premier international forum for the presentation of new research results, systems and techniques in the broad field of recommender systems. Recommendation is a particular form of information filtering, that exploits past behaviors and user similarities to generate a list of information items that is personally tailored to an end-user’s preferences. Since the emergence of recommender systems, a large majority of research focuses on objective accuracy criteria and less attention has been paid to how users interact with the system and the efficacy of interface designs from users’ perspectives. The field has reached a point where it is ready to look beyond algorithms, into users’ interactions, decision making processes, and overall experience.
The IntRS workshop focuses on human-centered recommender system design and application. The workshop goal is to improve users’ overall experience with recommender systems by integrating different theories of human decision making into the construction of recommender systems and exploring better interfaces for recommender systems.
The workshop follows successful workshops on the same topic organized at RecSys conferences in 2014 – 2018. The continuous aim of the workshop is to bring together researchers and practitioners around the topics of designing and evaluating novel intelligent interfaces for recommender systems in order to: (1) share research and techniques, including new design technologies and evaluation methodologies, (2) identify next key challenges in the area, and (3) identify emerging topics.
The 11 technical papers included in the proceedings were selected through a rigorous reviewing process, where each paper was reviewed by three PC members.
The IntRS chairs would like to thank the RecSys workshop chairs, Sandra Garcia and Christoph Trattner, for their guidance during the workshop organization. We also wish to thank all authors and all presenters, and the members of the program committee. All of them secured the workshop’s high quality standards.
Marco de Gemmis Alexander Felfernig Pasquale Lops John O’Donovan Giovanni Semeraro
Martijn C. Willemsen
IntRS 2019 Workshop Organization
Peter Brusilovsky, School of Information Sciences, University of Pittsburgh, USA Marco de Gemmis, Dept. of Computer Science, University of Bari Aldo Moro, Italy Alexander Felfernig, Institute for Software Technology, Graz University of
Technology, Austria Pasquale Lops, Dept. of Computer Science, University of Bari Aldo Moro, Italy John O’Donovan, Dept. of Computer Science, Univ. of California, Santa Barbara, USA Giovanni Semeraro, Dept. of Computer Science, University of Bari Aldo Moro, Italy Martijn C. Willemsen, Eindhoven University of Technology, The Netherlands
Marco de Gemmis, Dept. of Computer Science, University of Bari Aldo Moro, Italy Pasquale Lops, Dept. of Computer Science, University of Bari Aldo Moro, Italy
Pasquale Lops, Dept. of Computer Science, University of Bari Aldo Moro, Italy To Explain or not to Explain: the Effects of Personal Characteristics when Explaining Feature-based Recommendations in Different Domains Martijn Millecamp, Sidra Naveed, Katrien Verbert and Jürgen Ziegler Designing for Serendipity in a University Course Recommendation System Zach Pardos and Weijie Jiang Using Facial Recognition Services as Implicit Feedback for Recommenders Toon De Pessemier, Ine Coppens and Luc Martens Generation of Hints to Overcome Difficulty in Operating Interactive Recommender Systems Yuri Nakao, Takuya Ohwa and Kotaro Ohori Investigating Mechanisms for User Integration in the Activity Goal Recommendation Process by Interface Design Katja Herrmanny, Simone Löppenberg and Michael Schwarz
Short Papers Spotivibes: Tagging Playlist Vibes With Colors Hiba Abderrazik, Giovan Angela, Hans Brouwer, Henky Janse, Sterre Lutz, Gwennan Smitskamp, Sandy Manolios and Cynthia Liem Visualizing Ratings in Recommender System Datasets Diego Monti, Giuseppe Rizzo and Maurizio Morisio The Effectiveness of Advice Solicitation and Social Peers in an Energy Recommender System Alain Starke Towards Evaluating User Profiling Methods Based on Explicit Ratings on Item Features 72 Luca Luciano Costanzo, Yashar Deldjoo, Maurizio Ferrari Dacrema, Markus Schedl and Paolo Cremonesi Does the User Have A Theory of the Recommender? A Pilot Study Muheeb Faizan Ghori, Arman Dehpanah, Jonathan Gemmell, Hamed Qahri-Saremi and Bamshad Mobasher 1 10 19 28 36 46 55 60 65 77