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 iii iv 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 v vi 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 vii