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5 th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems

(IntRS) 2018

Vancouver, Canada, October 7

th

, 2018

Proceedings

edited by Peter Brusilovsky Marco de Gemmis Alexander Felfernig Pasquale Lops John O’Donovan Giovanni Semeraro Martijn C. Willemsen

in conjunction with

12

th

ACM Conference on Recommender Systems (RecSys 2018)

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© 2018. Copyright for the individual papers remains with the authors. Copying permitted for private and academic purposes. This volume is published and copyrighted by its editors.

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Preface

This volume contains the papers presented at the 5th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS), held as part of the 12th ACM Conference on Recommender System (RecSys), in Vancouver, Canada.

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 – 2017. 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. These papers cover the themes of Affective Factors, Novelty and Diversity, Decision Making and Visualization, Transparency and Explanations. The workshop additionally consists of an invited talk by Katrien Verbert on “Mixed-initiative Recommender Systems: Towards a Next Generation of Recommender Systems through User Involvement”.

The IntRS chairs would like to thank the RecSys workshop chairs, Martha Larson and Alejandro Bellogin, 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 2018

Peter Brusilovsky Marco de Gemmis Alexander Felfernig Pasquale Lops John O’Donovan Giovanni Semeraro Martijn C. Willemsen

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IntRS 2018 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 Chair: Marco de Gemmis, 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: Robin Burke, DePaul University Li Chen, Hong Kong Baptist University Jaegul Choo, Korea University

Michael Ekstrand, Boise State University

Gerhard Friedrich, Alpen-Adria-Universitaet Klagenfurt Sergiu Gordea, Austrian Institute of Technology

Denis Helic, KTI, TU Graz Dietmar Jannach, TU Dortmund Bart Knijnenburg, Clemson University Cataldo Musto, University of Bari Aldo Moro Fedelucio Narducci, University of Bari Aldo Moro Stefan Reiterer, TU Graz

Olga C. Santos, aDeNu Research Group (UNED) Christin Seifert, University of Twente

Luis Terán, University of Fribourg

Marko Tkalčič, Free University of Bozen-Bolzano Markus Zanker, Free University of Bozen-Bolzano

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Table of Contents

Invited Talk

Mixed-initiative Recommender Systems: Towards a Next Generation of Recommender Systems through User Involvement

Katrien Verbert

1

Affective Factors

Affective Computing and Bandits: Capturing Context in Cold Start Situations

Sebastian Oehme and Linus W. Dietz

2

Novelty and Diversity

A Diversity Adjusting Strategy with Personality for Music Recommendation

Feng Lu and Nava Tintarev

7 Risk "Attention" or "Adventure": A Qualitative Study of Novelty and Familiarity in Music Listening

Vikas Kumar, Sabirat Rubya, Joseph A. Konstan and Loren Terveen

15

MovieTweeters: An Interactive Interface to Improve Recommendation Novelty

IshanGhanmodeand NavaTintarev

24

Decision Making and Visualization

Analysis of User Behavior in Interfaces with Recommended Items: An Eye-tracking Study

Peter Gaspar, Michal Kompan, Jakub Simko and Maria Bielikova

32

From Recommendation to Curation: When the System Becomes your Personal Docent

Nevena Dragovic, Ion Madrazo Azpiazu and Maria Soledad Pera

37 Online Daters’ Willingness to Use Recommender Technology for Mate Selection

Decisions

Stephanie Tom Tong, Elena F. Corriero, Robert G. Matheny and Jeffrey T. Hancock

45

Using Visualizations to Encourage Blind-Spot Exploration

Jayachithra Kumar and Nava Tintarev

53

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Transparency and Explanations

Assessing the Value of Transparency in Recommender Systems: An End-User Perspective

Eric S. Vorm and Andrew D. Miller

61

Towards Explanations for Visual Recommender Systems of Artistic Images

Vicente Domínguez, Pablo Messina, Christoph Trattner and Denis Parra

69 Understanding how to Explain Package Recommendations in the Clothes Domain

Agung Toto Wibowo, Advaith Siddharthan, Judith Masthoff and Chenghua Lin

74

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