Deep generative models for collaborative filtering with multi-source and high dimensional data = 여러 종류의 다차원 데이터를 가지는 협업 필터링을 위한 딥 생성 모델

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This thesis examines hybrid collaborative filtering, which is a class of the collaborative filtering methods with multiple high dimensional auxiliary information. Regarding methodology, we consider structured probabilistic models and deep generative models. In the first chapter, we look at structured probabilistic models for phenotyping with electronic health records in the field of medical informatics. In the second chapter, we explore the modeling aspects of deep generative models for hybrid collaborative filtering. Finally, the third chapter deals with a hybrid collaborative filtering model considering domain characteristics. We propose and compare structured probabilistic models and deep generative models for hybrid collaborative filtering. In particular, we cover various modeling aspects in terms of deep generative models and compare them empirically. These results can be useful guidelines for future research and are expected to contribute to the expansion of the relevant fields. We also propose a hybrid recurrent collaborative filtering model for sequential diagnosis prediction that considers the medical domain characteristics. This effort can contribute to overcoming the limitations of static collaborative filtering methods.
Advisors
Moon, Il-Chulresearcher문일철researcher
Description
한국과학기술원 :산업및시스템공학과,
Publisher
한국과학기술원
Issue Date
2018
Identifier
325007
Language
eng
Description

학위논문(박사) - 한국과학기술원 : 산업및시스템공학과, 2018.8,[vii, 94 p. :]

Keywords

collaborative filtering▼adeep generative models▼amedical informatics▼arecommender sytstems▼adiagnosis prediction; 협업 필터링▼a딥 생성 모델▼a의료 정보학▼a추천 시스템▼a질병 예측

URI
http://hdl.handle.net/10203/264733
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=827893&flag=dissertation
Appears in Collection
IE-Theses_Ph.D.(박사논문)
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