ERASE: Benchmarking Feature Selection Methods for Deep Recommender Systems-Reference-Cited by-同舟云学术

ERASE: Benchmarking Feature Selection Methods for Deep Recommender Systems

Published:2024-08-24 Issue: Volume:20 Page:5194-5205
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Container-title:Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Jia Pengyue¹^ORCID,Wang Yejing¹^ORCID,Du Zhaocheng²^ORCID,Zhao Xiangyu¹^ORCID,Wang Yichao²^ORCID,Chen Bo²^ORCID,Wang Wanyu¹^ORCID,Guo Huifeng²^ORCID,Tang Ruiming²^ORCID

Affiliation:

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

2. Huawei Noah's Ark Lab, Shenzhen, China

Funder

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

APRC - CityU New Research Initiatives

Huawei (Huawei Innovation Research Program)

Research Impact Fund

CityU - HKIDS Early Career Research Grant

Hong Kong Environmental and Conservation Fund

SIRG - CityU Strategic Interdisciplinary Research Grant

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3637528.3671571

Reference52 articles.

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3. Benchmark for filter methods for feature selection in high-dimensional classification data

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5. A Comprehensive Survey on Automated Machine Learning for Recommendations