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Published, Version of Record (VoR)Open Access via Drexel Libraries Read and Publish Program 2025CC BY V4.0, Open
Conference proceeding
Conducting Recommender Systems User Studies Using POPROX
Adjunct Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalization, pp 1-2
16 Jun 2025
Featured in Collection : Research Supported by Drexel Libraries' OA Programs
Abstract
The Platform for OPen Recommendation and Online eXperimentation (POPROX) is a new resource to allow recommender systems and personalization researchers to conduct online user research without having to develop all of the necessary infrastructure and recruit users themselves. Our first domain is personalized news recommendations: POPROX 1.0 provides a daily newsletter (with content from the Associated Press) to users who have already consented to participate in research, along with interfaces and protocols to support researchers in conducting studies that assign subsets of users to various experimental algorithms and/or interfaces.
The purpose of this tutorial is to introduce the platform and its capabilities to researchers in the UMAP community who may be interested using the system. Participants will walk through the implementation of a sample experiment to demonstrate the mechanics of designing and running user studies with POPROX.
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Details
- Title
- Conducting Recommender Systems User Studies Using POPROX
- Creators
- Robin Burke - University of Colorado BoulderMichael Ekstrand (Corresponding Author) - Drexel University
- Publication Details
- Adjunct Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalization, pp 1-2
- Conference
- UMAP '25: 33rd ACM Conference on User Modeling, Adaptation and Personalization
- Series
- ACM Conferences
- Publisher
- Association for Computing Machinery
- Number of pages
- 2
- Grant note
- U.S. National Science Foundation: 22-32555, 24-15042
The POPOROX platform is supported by the U.S. National Science Foundation under Grant Nos. 22-32555 and 24-15042.
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:001525524600001
- Scopus ID
- 2-s2.0-105011085736
- Other Identifier
- 991022057777904721
InCites Highlights
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Information Systems
- Computer Science, Interdisciplinary Applications
- Computer Science, Theory & Methods