2nd Workshop on Online and Adaptive Recommender Systems (OARS)

被引：0

作者：

Cui, Xiquan ^{[1
]}

Dave, Vachik ^{[2
]}

Su, Yi ^{[3
]}

Al-Jadda, Khalifeh ^{[1
]}

Kumar, Srijan ^{[4
]}

McAuley, Julian ^{[5
]}

Ye, Tao ^{[6
]}

Aryafar, Kamelia ^{[7
]}

Korayem, Mohammad ^{[8
]}

机构：

[1] Home Depot, Atlanta, GA 30324 USA

[2] Walmart Labs, Sunnyvale, CA USA

[3] Univ Calif Berkeley, Berkeley, CA USA

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

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

[6] Amazon, San Francisco, CA USA

[7] Google, Mountain View, CA USA

[8] CareerBuilder, San Francisco, CA USA

来源：

PROCEEDINGS OF THE 28TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, KDD 2022 | 2022年

关键词：

D O I：

10.1145/3534678.3542893

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Recommender systems (RecSys) play important roles in helping users navigate, discover, and consume large and highly dynamic information. Today, many RecSys solutions deployed in the real world rely on categorical user-profiles and/or pre-calculated recommendation actions that stay static during a user session. However, recent trends suggest that RecSys need to model user intent in real time and constantly adapt to meet user needs at the moment or change user behavior in situ. There are three primary drivers for this emerging need of online adaptation. First, in order to meet the increasing demand for a better personalized experience, the personalization dimensions and space will grow larger and larger. It would not be feasible to pre-compute recommended actions for all personalization scenarios beyond a certain scale. Second, in many settings the system does not have user prior history to leverage. Estimating user intent in real time is the only feasible way to personalize. As various consumer privacy laws tighten, it is foreseeable that many businesses will reduce their reliance on static user profiles. Therefore, it makes the modeling of user intent in real time an important research topic. Third, a user's intent often changes within a session and between sessions, and user behavior could shift significantly during dramatic events. A RecSys should adapt in real time to meet user needs and be robust against distribution shifts. The online and adaptive recommender systems (OARS) workshop offers a focused discussion of the study and application of OARS, and will bring together an interdisciplinary community of researchers and practitioners from both industry and academia. KDD, as the premier data science conference, is an ideal venue to gather leaders in the field to further research into OARS and promote its adoption. This workshop is complementary to several sessions of the main conference (e.g., recommendation, reinforcement learning, etc.) and brings them together using a practical and focused application.

引用

页码：4862 / 4863

页数：2