Workshop on Online and Adaptative Recommender Systems (OARS)

被引：0

作者：

Cui, Xiquan ^{[1
]}

Afshar, Estelle ^{[1
]}

Al-Jadda, Khalifeh ^{[1
]}

Kumar, Srijan ^{[2
]}

McAuley, Julian ^{[3
]}

Ye, Tao ^{[4
]}

Aryafar, Kamelia ^{[5
]}

Dave, Vachik ^{[6
]}

Korayem, Mohammad ^{[7
]}

机构：

[1] Home Depot, Atlanta, GA 30308 USA

[2] Georgia Inst Technol, Atlanta, GA 30332 USA

[3] Univ Calif San Diego, San Diego, CA USA

[4] Amazon, San Francisco, CA USA

[5] Google, Mountain View, CA 94043 USA

[6] Walmart Labs, Sunnyvale, CA USA

[7] CareerBuilder, Atlanta, GA USA

来源：

KDD '21: PROCEEDINGS OF THE 27TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING | 2021年

关键词：

Recommender systems; online; adaptive; real-time;

D O I：

10.1145/3447548.3469472

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Many recommender systems deployed in the real world rely on categorical user-profiles and/or pre-calculated recommendation actions that stay static during a user session. Recent trends suggest that recommender systems should model user intent in real time and constantly adapt to meet user needs at the moment or change user behavior in situ. In addition, there have been many advances that make online and adaptive recommender systems (OARS) feasible, scalable, and more sophisticated. This workshop aims to bring together practitioners and researchers from academia and industry to discuss the challenges and approaches to implement OARS algorithms and systems and improve user experiences by better modeling and responding to user intent.

引用

页码：4116 / 4117

页数：2

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