Alexander Felfernig in conjunction with 14th ACM Conference on Recommender Systems (RecSys 2020)
edited by
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).
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.
Marco de Gemmis Alexander Felfernig Pasquale Lops John O’Donovan Giovanni Semeraro Martijn C. Willemsen Chairs:
IntRS 2020 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 John O’Donovan, Dept. of Computer Science, Univ. of California, Santa Barbara, USA
Pasquale Lops, Dept. of Computer Science, University of Bari Aldo Moro, Italy
Long Papers User Feedback in Controllable and Explainable Social Recommender Systems: a Linguistic Analysis Chun-Hua Tsai and Peter Brusilovsky Featuristic: An interactive hybrid system for generating explainable recommendations – beyond system accuracy Sidra Naveed and Jürgen Ziegler Post-hoc Explanations for Complex Model Recommendations using Simple Methods Dorin Shmaryahu, Guy Shani and Bracha Shapira A Comparison of Services for Intent and Entity Recognition for Conversational 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 Alaa Alslaity and Thomas Tran Exploiting Distributional Semantics Models for Natural Language Context-aware 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? Dietmar Jannach and Ahtsham Manzoor