Abstract
Creating efficient and effective search and recommendation algorithms has been the main objective of industry practitioners and academic researchers over the years. However, recent research has shown how these algorithms trained on historical data lead to models that might exacerbate existing biases and generate potentially negative outcomes. Defining, assessing, and mitigating these biases throughout experimental pipelines is a primary step for devising search and recommendation algorithms that can be responsibly deployed in real-world applications. This workshop aims to collect novel contributions in this field and offer a common ground for interested researchers and practitioners. More information about the workshop is available at https://biasinrecsys.github.io/sigir2024/
Original language | English |
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Title of host publication | SIGIR 2024 - Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval |
Publisher | Association of Computing Machinery |
Pages | 3033-3035 |
Number of pages | 3 |
ISBN (Electronic) | 9798400704314 |
DOIs | |
Publication status | Published - 10 Jul 2024 |
Event | 47th International ACM SIGIR Conference on Research and Development in Information Retrieval: SIGIR 2024 - Washington, United States Duration: 14 Jul 2024 → 18 Jul 2024 |
Conference
Conference | 47th International ACM SIGIR Conference on Research and Development in Information Retrieval |
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Abbreviated title | SIGIR 2024 |
Country/Territory | United States |
City | Washington |
Period | 14/07/24 → 18/07/24 |
Keywords
- algorithms
- bias
- fairness
- recommendation
- search
ASJC Scopus subject areas
- Information Systems
- Software