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https://ceur-ws.org/Vol-2682/preface.pdf
7th Joint Workshop on Interfaces and Human
Decision Making for Recommender Systems
(IntRS) 2020
Online Event, September 26th, 2020
Proceedings
edited by Peter Brusilovsky
Marco de Gemmis
Alexander Felfernig
Pasquale Lops
John O’Donovan
Giovanni Semeraro
Martijn C. Willemsen
in conjunction with
14th ACM Conference on Recommender Systems (RecSys 2020)
Copyright © 2020 for the individual papers by the papers' authors. Copyright © 2020 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 7th Joint Workshop on Interfaces and Human Decision Making
for Recommender Systems (IntRS), held as part of the 14th ACM Conference on Recommender System (RecSys).
The event was planned in Rio de Janeiro but, due to the COVID-19 emergency, it was held online.
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 –
2019. 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 9 technical papers included in the proceedings were selected among 11 submissions, through a rigorous
reviewing process, where each paper was reviewed by three PC members.
The IntRS chairs would like to thank the RecSys 2020 workshop chairs, Jussara Almeida and Pablo Castells,
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 2020 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
John O’Donovan, Dept. of Computer Science, Univ. of California, Santa Barbara, USA
Web Chair: Pasquale Lops, Dept. of Computer Science, University of Bari Aldo Moro, Italy
Program Committee: Ludovico Boratto, Eurecat
Robin Burke, University of Colorado Boulder
Amra Delić, TU Wien
Peter Dolog, Aalborg University
Michael Ekstrand, Boise State University
Sergiu Gordea, Austrian Institute of Technology
Denis Helic, KTI, TU Graz
Andreas Holzinger, Medical University and Graz University of Technology
Dietmar Jannach, University of Klagenfurt
Elisabeth Lex, Graz University of Technology
Cataldo Musto, University of Bari Aldo Moro
Julia Neidhardt, Vienna University of Technology
Behnam Rahdari, University of Pittsburgh
Olga C. Santos, aDeNu Research Group (UNED)
Alain Starke, Eindhoven University of Technology
Luis Terán, University of Fribourg
Marko Tkalčič, University of Primorska
Chun-Hua Tsai, The Pennsylvania State University
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
User Feedback in Controllable and Explainable Social Recommender Systems: a 1
Linguistic Analysis
Chun-Hua Tsai and Peter Brusilovsky
Featuristic: An interactive hybrid system for generating explainable recommendations – 14
beyond system accuracy
Sidra Naveed and Jürgen Ziegler
Post-hoc Explanations for Complex Model Recommendations using Simple Methods 26
Dorin Shmaryahu, Guy Shani and Bracha Shapira
A Comparison of Services for Intent and Entity Recognition for Conversational 37
Recommender Systems
Andrea Iovine, Fedelucio Narducci, Marco de Gemmis, Marco Polignano, Pierpaolo Basile and Giovanni
Semeraro
The Effect of Personality Traits on Persuading Recommender System Users 48
Alaa Alslaity and Thomas Tran
Diversity Exposure in Social Recommender Systems: A Social Capital Theory 57
Perspective
Chun-Hua Tsai, Jukka Huhtamäki, Thomas Olsson and Peter Brusilovsky
Exploiting Distributional Semantics Models for Natural Language Context-aware 65
Justifications for Recommender Systems
Cataldo Musto, Giuseppe Spillo, Marco de Gemmis, Pasquale Lops and Giovanni Semeraro
Short Papers
End-to-End Learning for Conversational Recommendation: A Long Way to Go? 72
Dietmar Jannach and Ahtsham Manzoor
Recommending Interesting Writing using a Controllable, Explanation-Aware Visual 77
Interface
Rohan Bansal, Jordan Olmstead, Uri Bram, Robert Cottrell, Gabriel Reder and Jaan Altosaar
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