○ 발표 제목: Bayesian Topic Models for Fashion Item Recommendation
○ 일시: 2021년 12월 2일(목) 16:00-17:00
○ 발표자: 채민우 교수(산업경영공학과, POSTECH)
○ ZOOM Meeting ID: 832 6600 9463
https://snu-ac-kr.zoom.us/j/
○ 초록: Recent advances in machine learning have provided valuable tools for constructing various recommendation systems in e-commerce companies such as Amazon and eBay. In this talk, we consider click history records from an online fashion mall using a well-known Bayesian topic model, the latent Dirichlet allocation (LDA). Although LDA has popularly been used in the recommendation, a naive algorithm based on the LDA in fashion item recommendation may yield a crucial issue. For a customer who clicked pants primarily, for example, the algorithm tends to recommend pants only. Given a click history of pants, a more desirable algorithm would recommend fashion items compatible with the clicked pants, such as T-shirts, jumpers, and shoes. For this purpose, we propose an algorithm based on a novel Bayesian model, called the group-constrained LDA, which incorporates prior information about the item groups. The proposed method is applied to the click history data from SSF Shop, one of the largest online fashion malls in South Korea.
○ 발표자 약력:
- Assistant Professor at Department of industrial and Management Engineering, POSTECH (current)
- Assistant Professor at Department of Mathematics, Applied Mathematics and Statistics, Case Western Reserve Universe
- Lecturer at Department of Statistics, Seoul National University and Department of Statistics, University of Seoul
- Postdoctoral Fellow at Department of Mathematics, University of Texas
- Lecturer at Department of Statistics, Seoul National University and Department of Statistics, University of Seoul
* Education
- Ph.D. in Statistics at Seoul National University
- B.S. in Mathematics at POSTECH.
○ 주최: 데이터 과학 분야 인공지능 센터
○ 지원: 서울대학교 AI연구원 선도혁신 연구센터 지원사업
○ 문의: 이종진 조교(ga0408@snu.ac.kr)