Notice
- 코로나19 영향으로 비대면강의실시로 Zoom를 통해 강의를 실시합니다.
- Zoom 강의는 ETL-데이터베이스특강에서 접속할 수 있습니다.
- 대부분의 공지사항은 보드 링크 또는 ETL에 공지되니 꼭 확인해주세요.
- 초안지 구글링크 신청 시, 반드시 ETL에서 해당 과목을 청강생 신청하고 첫날 수업에 참여해야 초안지 승인 자격이 주어집니다.
Overview
In this era of infinite choices of information, media contents, and consumer products and services, recommendation has become an indispensable function in our everyday lives. A recommendation system is a computerized system that seeks to predict the preference of a user towards an item. As the range of applications of recommender systems is wide, the spectrum of technologies employed by these systems is truly diverse, ranging from simple content matching to matrix/tensor operations, graph analysis, and also deep neural networks. In this course, we study the basic building blocks of recommender systems along with the various relevant technologies.
A concrete computer science background with a command of machine learning principles is required. There will be multiple programming assignments, exams, and class presentations.
References
- Recommender Systems: The Textbook, by Charu C. Aggarwal, Springer, 2016
- Recommender System Handbook, by F. Ricci , L. Rokach, B. Shapira. P. Kantor (Eds.), Springer, 2011
- Selected research papers