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Published, Version of Record (VoR)CC BY V4.0, Open
Conference paper
What News Recommendation Research Did (But Mostly Didn’t) Teach Us About Building A News Recommender
CEUR workshop proceedings, v 4063
Sep 2025
Abstract
One of the goals of recommender systems research is to provide insights and methods that can be used by practitioners to build real-world systems that deliver high-quality recommendations to actual people grounded in their genuine interests and needs. We report on our experience trying to apply the news recommendation literature to build POPROX, a live platform for news recommendation research, and reflect on the extent to which the current state of research supports system-building efforts. Our experience highlights several unexpected challenges encountered in building personalization features that are commonly found in products from news aggregators and publishers, and shows how those difficulties are connected to surprising gaps in the literature. Finally, we offer a set of lessons learned from building a live system with a persistent user base and highlight opportunities to make future news recommendation research more applicable and impactful in practice.
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Details
- Title
- What News Recommendation Research Did (But Mostly Didn’t) Teach Us About Building A News Recommender
- Creators
- Karl Higley - University of MinnesotaRobin Burke - University of Colorado BoulderMichael D. Ekstrand - Drexel University, Information ScienceBart P. Knijnenburg - Clemson University
- Publication Details
- CEUR workshop proceedings, v 4063
- Conference
- Beyond Algorithms: Reclaiming the Interdisciplinary Roots of Recommender Systems Workshop (BEYOND 2025) (Prague, Czech Republic, 26 Sep 2025–26 Sep 2025)
- Number of pages
- 15
- Grant note
- 22-32555 / National Science Foundation (100000001) 22-32552 / National Science Foundation (100000001) 22-32551 / National Science Foundation (100000001) 24-09199 / National Science Foundation (100000001)
- Resource Type
- Conference paper
- Language
- English
- Academic Unit
- Information Science
- Scopus ID
- 2-s2.0-105019518823
- Other Identifier
- 991022129388004721