| Vol-3815 urn:nbn:de:0074-3815-4 Copyright © 2024 for the individual papers by the papers' authors. Copyright © 2024 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).

# IntRS 2024
Interfaces and Human Decision Making for Recommender Systems 2024

### Proceedings of the 11th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems
co-located with 18th ACM Conference on Recommender Systems (RecSys 2024)

### Hybrid Event, Bari, October 18, 2024.

Edited by

### Peter Brusilovsky *
Marco de Gemmis **
Alexander Felfernig ***
Marco Polignano **
Giovanni Semeraro **
Martijn C. Willemsen ****

* University of Pittsburgh, School of Information Sciences, Pittsburgh, USA
** University of Bari Aldo Moro, Department of Computer Science, Bari, Italy
*** Graz University of Technology, Institute for Software Technology, Graz, Austria
**** Eindhoven University of Technology, Department of Industrial Engineering and Innovation Sciences, Eindhoven, The Netherlands

## Table of Contents

- Preface
  Summary: There were 15 papers submitted for peer-review to this workshop. Out of these, 11 papers were included in this volume, 4 as regular papers and 7 as short papers.

### Regular Papers

- Comparative Explanations for Recommendation: Research Directions 1-14
  Meysam Varasteh, Elizabeth McKinnie, Amanda Aird, Daniel Acuña, Robin Burke
- Comparing User Interfaces for Customizing Multi-Objective Recommender Systems 15-28
  Patrik Dokoupil, Ludovico Boratto, Ladislav Peska
- Designing and Evaluating an Educational Recommender System with Different Levels of User Control 29-45
  Qurat Ul Ain, Mohamed Amine Chatti, William Kana Tsoplefack, Rawaa Alatrash, Shoeb Joarder
- Bridging the Transparency Gap: Exploring Multi-Stakeholder Preferences for Targeted Advertisement Explanations 46-58
  Dina Zilbershtein, Francesco Barile, Daan Odijk, Nava Tintarev

### Short Papers

- What Are We Optimizing For? A Human-centric Evaluation of Deep Learning-based Movie Recommenders 59-70
  Ruixuan Sun, Xinyi Wu, Avinash Akella, Ruoyan Kong, Bart Knijnenburg, Joseph A. Konstan
- Designing an Interpretable Interface for Contextual Bandits 71-80
  Andrew Maher, Matia Gobbo, Lancelot Lachartre, Subash Prabanantham, Rowan Swiers, Puli Liyanagama
- Social Circle-Enhanced Fashion Recommendations System 81-91
  Aayesha Aayesha, Muhammad Afzaal, Julia Neidhardt
- The Effect of Relational versus Anecdotal Explanations in Movie Domain Recommendations 92-102
  Liam de la Cour, Derek Bridge
- Intended Movie Experience: Linking Elicited Emotions to Eudaimonic and Hedonic Characteristics 103-112
  Arsen Matej Golubovikj, Osnat Mokryn, Marko Tkalčič
- The Importance of Cognitive Biases in the Recommendation Ecosystem: Evidence of Feature-Positive Effect, Ikea Effect, and Cultural Homophily 113-123
  Markus Schedl, Oleg Lesota, Shahed Masoudian
- Integrating the Mechanisms of Critiquing-based Recommendation into Constraint Solving 124-133
  Pavle Knežević, Alexander Felfernig, Sebastian Lubos

2024-10-24: submitted by Marco de Gemmis, metadata incl. bibliographic data published under Creative Commons CC0
2024-10-31: published on CEUR Workshop Proceedings (CEUR-WS.org, ISSN 1613-0073) |valid HTML5|