Augmenting Netflix Search with In-Session Adapted Recommendations

被引：6

作者：

Bhattacharya, Moumita ^{[1
]}

Lamkhede, Sudarshan ^{[1
]}

机构：

[1] Netflix Res, Los Gatos, CA 95032 USA

来源：

PROCEEDINGS OF THE 16TH ACM CONFERENCE ON RECOMMENDER SYSTEMS, RECSYS 2022 | 2022年

关键词：

Recommender Systems; Search; Neural Networks; Multi-task Learning; Sequence Models;

D O I：

10.1145/3523227.3547407

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We motivate the need for recommendation systems that can cater to the members' in-the-moment intent by leveraging their interactions from the current session. We provide an overview of an end-to-end in-session adaptive recommendations system in the context of Netflix Search. We discuss the challenges and potential solutions when developing such a system at production scale.

引用

页码：542 / 545

页数：4

共 10 条

[1] User Response Models to Improve a REINFORCE Recommender System [J].

Chen, Minmin ;

Chang, Bo ;

Xu, Can ;

Chi, Ed H. .

WSDM '21: PROCEEDINGS OF THE 14TH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, 2021, :121-129

[2] Exploring Longitudinal Effects of Session-based Recommendations [J].

Ferraro, Andres ;

Jannach, Dietmar ;

Serra, Xavier .

RECSYS 2020: 14TH ACM CONFERENCE ON RECOMMENDER SYSTEMS, 2020, :474-479

[3]

Hidasi B, 2016, Arxiv, DOI [arXiv:1511.06939, DOI 10.48550/ARXIV.1511.06939]

[4] Self-Attentive Sequential Recommendation [J].

Kang, Wang-Cheng ;

McAuley, Julian .

2018 IEEE INTERNATIONAL CONFERENCE ON DATA MINING (ICDM), 2018, :197-206

[5] Challenges in Search on Streaming Services: Netflix Case Study [J].

Lamkhede, Sudarshan ;

Das, Sudeep .

PROCEEDINGS OF THE 42ND INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '19), 2019, :1371-1374

[6]

Liebling DJ, 2012, SIGIR 2012: PROCEEDINGS OF THE 35TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P1035, DOI 10.1145/2348283.2348456

[7] STAMP: Short-Term Attention/Memory Priority Model for Session-based Recommendation [J].

Liu, Qiao ;

Zeng, Yifu ;

Mokhosi, Refuoe ;

Zhang, Haibin .

KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, 2018, :1831-1839

[8] ADER: Adaptively Distilled Exemplar Replay Towards Continual Learning for Session-based Recommendation [J].

Mi, Fei ;

Lin, Xiaoyu ;

Faltings, Boi .

RECSYS 2020: 14TH ACM CONFERENCE ON RECOMMENDER SYSTEMS, 2020, :408-413

[9] Deep learning for recommender systems: A Netflix case study [J].

Steck, Harald ;

Baltrunas, Linas ;

Elahi, Ehtsham ;

Liang, Dawen ;

Raimond, Yves ;

Basilico, Justin .

AI MAGAZINE, 2021, 42 (03) :7-18

[10] Using Navigation to Improve Recommendations in Real-Time [J].

Wu, Chao-Yuan ;

Alvino, Christopher, V ;

Smola, Alexander J. ;

Basilico, Justin .

PROCEEDINGS OF THE 10TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS'16), 2016, :341-348

← 1 →