Recommender Systems: A Review

• 成果类型: Review
• 署名作者: LeBlanc, Patrick M.; Banks, David; Fu, Linhui; Li, Mingyan; Tang, Zhengyu; Wu, Qiuyi
• 署名单位: Duke University; University of North Carolina; University of North Carolina Greensboro; University of Rochester
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2023.2279695
• 发表日期: 2024
• 页码: 773-785
• 关键词: of-the-art

摘要

Recommender systems are the engine of online advertising. Not only do they suggest movies, music, or romantic partners, but they also are used to select which advertisements to show to users. This paper reviews the basics of recommender system methodology and then looks at the emerging arena of active recommender systems.