Notice
- 모든 공지사항은 ETL에 공지되니 꼭 확인해주세요.
- 모든 과제는 ETL로 제출해주세요.
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