| Vol-2462 urn:nbn:de:0074-2462-4 Copyright © 2019 for the individual papers by the papers' authors. Copyright © 2019 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).

# ImpactRS 2019
Impact of Recommender Systems 2019

### Proceedings of the 1st Workshop on the Impact of Recommender Systems
co-located with 13th ACM Conference on Recommender Systems (ACM RecSys 2019)

### Copenhagen, Denmark, September 19, 2019.

Edited by

### Oren Sar Shalom *
Dietmar Jannach **
Ido Guy ***

* Intuit AI, Israel
** University of Klagenfurt, Austria
*** eBay Research, Israel

## Table of Contents

- Preface

### Papers

- A Model for Evaluating Popularity and Semantic Information Variations in Radio Listening Sessions
  Lorenzo Porcaro, Emilia Gomez
- Assessing the Impact of a User-Item Collaborative Attack on Class of Users
  Yashar Deldjoo, Tommaso Di Noia, Felice Antonio Merra
- Does the Bubble Go Beyond? An Exploration of the Urban Filter Bubble
  Annelien Smets, Eladio Montero, Pieter Ballon
- Recommendations for Long-Term Profit Optimization
  Patrick Hosein, Inzamam Rahaman, Keanu Nichols, Kiran Maharaj
- The Impact of Recommenders on Scientific Article Discovery: The Case of Mendeley Suggest
  Minh Le, Subhradeep Kayal, Andrew Douglas
- Towards Creating a Non-synthetic Group Recommendation Dataset
  Matthijs Rijlaarsdam, Sebastiaan Scholten, Cynthia C.S. Liem

### Position papers

- Analysing Recommender Systems Impact on Users’ Choices
  Naieme Hazrati, Mehdi Elahi, Francesco Ricci
- Here, There, and Everywhere: Building a Scaffolding for Children's Learning through Recommendations
  Ashlee Milton, Emiliana Murgia, Monica Landoni, Theo Huibers, Maria Soledad Pera
- Leveraging Multi-Method Evaluation for Multi-Stakeholder Settings
  Christine Bauer, Eva Zangerle
- Measuring the Impact of Recommender Systems - A Position Paper on Item Consumption in User Studies
  Benedikt Loepp, Jürgen Ziegler
- Up Close, but not too Personal: Hypotargeting for Recommender Systems
  Martha Larson, Manel Slokom
- Towards More Impactful Recommender Systems Research
  Dietmar Jannach, Oren Sar Shalom, Joseph A. Konstan

2019-10-03: submitted by Dietmar Jannach, metadata incl. bibliographic data published under Creative Commons CC0
2019-10-05: published on CEUR-WS.org |valid HTML5|