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https://ceur-ws.org/Vol-2450/Preface.pdf
6th Joint Workshop on Interfaces and Human
Decision Making for Recommender Systems
(IntRS) 2019
Copenhagen, Denmark, September 19th, 2019
Proceedings
edited by Peter Brusilovsky
Marco de Gemmis
Alexander Felfernig
Pasquale Lops
John O’Donovan
Giovanni Semeraro
Martijn C. Willemsen
in conjunction with
13th ACM Conference on Recommender Systems (RecSys 2019)
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).
ii
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.
September 2019
Peter Brusilovsky
Marco de Gemmis
Alexander Felfernig
Pasquale Lops
John O’Donovan
Giovanni Semeraro
Martijn C. Willemsen
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IntRS 2019 Workshop Organization
Chairs: 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
Proceedings Chairs: 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
Web Chair: Pasquale Lops, Dept. of Computer Science, University of Bari Aldo Moro, Italy
Program Committee: Muesluem Atas, Graz University
Christine Bauer, Johannes Kepler University Linz
Ludovico Boratto, Eurecat
Amra Delić, TU Wien
Michael Ekstrand, Boise State University
Gerhard Friedrich, Alpen-Adria-Universitaet Klagenfurt
Sergiu Gordea, Austrian Institute of Technology
Denis Helic, KTI, TU Graz
Dietmar Jannach, University of Klagenfurt
Gerhard Leitner, University of Klagenfurt
Elisabeth Lex, Graz University of Technology
Bamshad Mobasher, DePaul University
Cataldo Musto, University of Bari Aldo Moro
Fedelucio Narducci, University of Bari Aldo Moro
Julia Neidhardt, Vienna University of Technology
Francesco Ricci, Free University of Bozen-Bolzano
Olga C. Santos, aDeNu Research Group (UNED)
Christin Seifert, University of Twente
Luis Terán, University of Fribourg
Marko Tkalčič, Free University of Bozen-Bolzano
Katrien Verbert, Katholieke Universiteit Leuven
Wolfgang Wörndl, Technical University of Munich
Markus Zanker, Free University of Bozen-Bolzano
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Table of Contents
Long Papers
How Playlist Evaluation Compares to Track Evaluations in Music Recommender 1
Systems
Sophia Hadash, Yu Liang and Martijn Willemsen
To Explain or not to Explain: the Effects of Personal Characteristics when Explaining 10
Feature-based Recommendations in Different Domains
Martijn Millecamp, Sidra Naveed, Katrien Verbert and Jürgen Ziegler
Designing for Serendipity in a University Course Recommendation System 19
Zach Pardos and Weijie Jiang
Using Facial Recognition Services as Implicit Feedback for Recommenders 28
Toon De Pessemier, Ine Coppens and Luc Martens
Generation of Hints to Overcome Difficulty in Operating Interactive Recommender 36
Systems
Yuri Nakao, Takuya Ohwa and Kotaro Ohori
Investigating Mechanisms for User Integration in the Activity Goal Recommendation 46
Process by Interface Design
Katja Herrmanny, Simone Löppenberg and Michael Schwarz
Short Papers
Spotivibes: Tagging Playlist Vibes With Colors 55
Hiba Abderrazik, Giovan Angela, Hans Brouwer, Henky Janse, Sterre Lutz, Gwennan Smitskamp, Sandy
Manolios and Cynthia Liem
Visualizing Ratings in Recommender System Datasets 60
Diego Monti, Giuseppe Rizzo and Maurizio Morisio
The Effectiveness of Advice Solicitation and Social Peers in an Energy Recommender 65
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 77
Muheeb Faizan Ghori, Arman Dehpanah, Jonathan Gemmell, Hamed Qahri-Saremi and Bamshad
Mobasher
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