MvFS: Multi-view Feature Selection for Recommender System-Reference-Cited by-同舟云学术

MvFS: Multi-view Feature Selection for Recommender System

Published:2023-10-21 Issue: Volume: Page:
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Container-title:Proceedings of the 32nd ACM International Conference on Information and Knowledge Management
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Author:

Lee Youngjune¹^ORCID,Jeong Yeongjong¹^ORCID,Park Keunchan¹^ORCID,Kang SeongKu²^ORCID

Affiliation:

1. NAVER Corporation, Seongnam, Republic of Korea

2. University of Illinois at Urbana-Champaign, Urbana, IL, USA

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3583780.3615243

Reference29 articles.

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3. Mark A Hall . 1999. Correlation-based feature selection for machine learning. Ph.,D. Dissertation . The University of Waikato . Mark A Hall. 1999. Correlation-based feature selection for machine learning. Ph.,D. Dissertation. The University of Waikato.

4. Ridge Regression: Biased Estimation for Nonorthogonal Problems

5. Robert A Jacobs , Michael I Jordan , Steven J Nowlan , and Geoffrey E Hinton . 1991. Adaptive mixtures of local experts. Neural computation , Vol. 3 , 1 ( 1991 ), 79--87. Robert A Jacobs, Michael I Jordan, Steven J Nowlan, and Geoffrey E Hinton. 1991. Adaptive mixtures of local experts. Neural computation, Vol. 3, 1 (1991), 79--87.

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1. ERASE: Benchmarking Feature Selection Methods for Deep Recommender Systems;Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining;2024-08-24

2. ProAdaFS: Probabilistic and Adaptive Feature Selection in Deep Recommendation Systems;2024 International Conference on Information Networking (ICOIN);2024-01-17