Mitigating Confounding Bias in Practical Recommender Systems With Partially Inaccessible Exposure Status-Reference-Cited by-同舟云学术

Mitigating Confounding Bias in Practical Recommender Systems With Partially Inaccessible Exposure Status

Author:

Cao Tianwei¹^ORCID,Xu Qianqian²^ORCID,Yang Zhiyong¹^ORCID,Huang Qingming¹^ORCID

Affiliation:

1. School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China

2. Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

Funder

National Key R&D Program of China

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Youth Innovation Promotion Association of the Chinese Academy of Sciences

Strategic Priority Research Program of Chinese Academy of Sciences

Innovation Funding of ICT, CAS

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Applied Mathematics,Artificial Intelligence,Computational Theory and Mathematics,Computer Vision and Pattern Recognition,Software

Link

Reference57 articles.

3. Probabilistic matrix factorization with non-random missing data;Hernández-Lobato

4. Collaborative filtering and the missing at random assumption;Marlin,2012