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Author: Jia Hua Jeng
RecSys '24: Proceedings of the 18th ACM Conference on Recommender Systems
Pages 1283 - 1289
Published: 08 October 2024 Publication History
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Abstract
News Recommender systems (NRSs) aid in decision-making in news media. However, undesired effects can emerge. Among these are selective exposures that may contribute to polarization, potentially reinforcing existing attitudes through belief perseverance—discounting contrary evidence due to their opposing attitudinal strength. This can be unsafe for people, making it difficult to accept information objectively. A crucial issue in news recommender system research is how to mitigate these undesired effects by designing recommender interfaces and machine learning models that enable people to consider to be more open to different perspectives. Alongside accurate models, the user experience is an equally important measure. Indeed, the core statistics are based on users’ behaviors and experiences in this research project. Therefore, this research agenda aims to steer the choices of readers’ based on altering their attitudes. The core methods plan to concentrate on the interface design and ML model building involving manipulations of cues, users’ behaviors prediction, NRSs algorithm and changing the nudges. In sum, the project aims to provide insight in the extent to which news recommender systems can be effective in mitigating polarized opinions.
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Index Terms
Bridging Viewpoints in News with Recommender Systems
Human-centered computing
Human computer interaction (HCI)
HCI design and evaluation methods
User models
User studies
Interaction design
Interaction design process and methods
User interface design
Information systems
Information retrieval
Retrieval tasks and goals
Recommender systems
Index terms have been assigned to the content through auto-classification.
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Published In
RecSys '24: Proceedings of the 18th ACM Conference on Recommender Systems
October 2024
1438 pages
ISBN:9798400705052
DOI:10.1145/3640457
- Editors:
- Tommaso Di Noia,
- Pasquale Lops,
- Thorsten Joachims,
- Katrien Verbert,
- Pablo Castells,
- Zhenhua Dong,
- Ben London
Copyright © 2024 Owner/Author.
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.
Sponsors
- SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web
- SIGAI: ACM Special Interest Group on Artificial Intelligence
- SIGKDD: ACM Special Interest Group on Knowledge Discovery in Data
- SIGIR: ACM Special Interest Group on Information Retrieval
- SIGCHI: ACM Special Interest Group on Computer-Human Interaction
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 08 October 2024
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Author Tags
- Attitude
- Behavioural Change
- News Recommender Systems
- Nudges
- Polarization
- Selective Exposure
Qualifiers
- Extended-abstract
- Research
- Refereed limited
Conference
RecSys '24
Sponsor:
- SIGWEB
- SIGAI
- SIGKDD
- SIGIR
- SIGCHI
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Overall Acceptance Rate 254 of 1,295 submissions, 20%
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