Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS’22)-Reference-Cited by-同舟云学术

Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS’22)

Published:2022-09-18 Issue: Volume: Page:
ISSN:
Container-title:Sixteenth ACM Conference on Recommender Systems
language:
Short-container-title:

Author:

Brusilovsky Peter¹,de Gemmis Marco²,Felfernig Alexander³,Lops Pasquale⁴,Polignano Marco⁵,Semeraro Giovanni⁶,Willemsen Martijn C.⁷

Affiliation:

1. School of Computing and Information, University of Pittsburgh, United States

2. Dept. of Computer Science, University of Bari Aldo Moro, Italy

3. Software Technology Institute, Graz University of Technology, Austria

4. Department of Computer Science, University of Bari Aldo Moro, Italy

5. University of Bari Aldo Moro, Italy

6. Dept. of Computer Science, University of Bari, Italy

7. Human-Technology Interaction, Eindhoven University of Technology, Netherlands

Publisher

ACM

Reference35 articles.

1. Joeran Beel and Haley Dixon . 2021 . The ’Unreasonable’ Effectiveness of Graphical User Interfaces for Recommender Systems. In Adjunct Publication of the 29th ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2021 , Utrecht, The Netherlands , June 21-25, 2021, Judith Masthoff, Eelco Herder, Nava Tintarev, and Marko Tkalcic (Eds.). ACM, 22–28. https://doi.org/10.1145/3450614.3461682 Joeran Beel and Haley Dixon. 2021. The ’Unreasonable’ Effectiveness of Graphical User Interfaces for Recommender Systems. In Adjunct Publication of the 29th ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2021, Utrecht, The Netherlands, June 21-25, 2021, Judith Masthoff, Eelco Herder, Nava Tintarev, and Marko Tkalcic (Eds.). ACM, 22–28. https://doi.org/10.1145/3450614.3461682

2. Learning with personalized recommender systems: A psychological view

3. Impacts of Personal Characteristics on User Trust in Conversational Recommender Systems

4. Generating Personalized Explanations for Recommender Systems Using a Knowledge Base

5. User perception of differences in recommender algorithms

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