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Published, Version of Record (VoR)Open Access via Drexel Libraries Read and Publish Program 2024CC BY V4.0, Open
Conference proceeding
Conducting Recommender Systems User Studies Using POPROX
Proceedings of the 18th ACM Conference on Recommender Systems, pp 1277-1278
08 Oct 2024
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 RecSys researchers to conduct online user research without having to develop all of the necessary infrastructure and recruit users. 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 prospective research users while walking through the implementation of a sample experiment so that researchers can proceed to propose and carry out experiments on the POPROX platform.
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1 citations in Scopus
Details
- Title
- Conducting Recommender Systems User Studies Using POPROX
- Creators
- Robin Burke - University of Colorado BoulderJoseph Konstan - University of MinnesotaMichael Ekstrand (Corresponding Author) - Drexel University, Information Science
- Publication Details
- Proceedings of the 18th ACM Conference on Recommender Systems, pp 1277-1278
- Conference
- RecSys '24: 18th ACM Conference on Recommender Systems
- Series
- ACM Conferences
- Publisher
- Association for Computing Machinery
- Number of pages
- 2
- Grant note
- U.S. National Science Foundation: 22-32551
The POPOROX platform is supported by the U.S. National Science Foundation under Grant No. 22-32551.
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:001336908500194
- Scopus ID
- 2-s2.0-85210520520
- Other Identifier
- 991021906507804721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- 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