Conference proceeding
Fairness and Discrimination in Recommendation and Retrieval
RECSYS 2019: 13TH ACM CONFERENCE ON RECOMMENDER SYSTEMS, pp 576-577
01 Jan 2019
Abstract
Fairness and related concerns have become of increasing importance in a variety of AI and machine learning contexts. They are also highly relevant to recommender systems and related problems such as information retrieval, as evidenced by the growing literature in RecSys, FAT*, SIGIR, and special sessions such as the FATREC and FACTS-IR workshops and the Fairness track at TREC 2019; however, translating algorithmic fairness constructs from classification, scoring, and even many ranking settings into recommendation and other information access scenarios is not a straightforward task. This tutorial will help orient RecSys researchers to algorithmic fairness, understand how concepts do and do not translate from other settings, and provide an introduction to the growing literature on this topic.
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Details
- Title
- Fairness and Discrimination in Recommendation and Retrieval
- Creators
- Michael D. Ekstrand - Boise State UniversityRobin Burke - University of Colorado SystemFernando Diaz - Microsoft Res, Montreal, PQ, CanadaAssoc Comp Machinery
- Publication Details
- RECSYS 2019: 13TH ACM CONFERENCE ON RECOMMENDER SYSTEMS, pp 576-577
- Publisher
- Assoc Computing Machinery
- Number of pages
- 2
- Grant note
- IIS 17-51278 / NSF; National Science Foundation (NSF)
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000557263400112
- Scopus ID
- 2-s2.0-85073379529
- Other Identifier
- 991021818498404721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Information Systems
- Operations Research & Management Science