MultiFS: Automated Multi-Scenario Feature Selection in Deep Recommender Systems-Reference-Cited by-同舟云学术

MultiFS: Automated Multi-Scenario Feature Selection in Deep Recommender Systems

Published:2024-03-04 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 17th ACM International Conference on Web Search and Data Mining
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Author:

Liu Dugang¹^ORCID,Yang Chaohua²^ORCID,Tang Xing³^ORCID,Wang Yejing⁴^ORCID,Lyu Fuyuan⁵^ORCID,Luo Weihong³^ORCID,He Xiuqiang³^ORCID,Ming Zhong⁶^ORCID,Zhao Xiangyu⁴^ORCID

Affiliation:

1. Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen University, Shenzhen, China

2. Shenzhen University, Shenzhen, China

3. Tencent, Shenzhen, China

4. City University of Hong Kong, Hong Kong, China

5. McGill University, Montreal, Canada

6. Shenzhen Technology University, Shenzhen, China

Funder

Hong Kong Environmental and Conservation Fund

National Natural Science Foundation of China

APRC - CityU New Research Initiatives

SIRG - CityU Strategic Interdisciplinary Research Grant

CityU - HKIDS Early Career Research Grant

Hong Kong ITC Innovation and Technology Fund Midstream Research Programme for Universities Project

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3616855.3635859

Reference39 articles.

1. Rich Caruana. 1997. Multitask learning. Machine learning 28, 1 (1997), 41--75.

2. PEPNet: Parameter and Embedding Personalized Network for Infusing with Personalized Prior Information

3. Making recommendations from multiple domains

4. CFS-MTL: A Causal Feature Selection Mechanism for Multi-task Learning via Pseudo-intervention

5. Wide & Deep Learning for Recommender Systems

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