Description
The purpose of this graduate-level course is introducing a distributed application system and understanding the related issues. In this semester, the main topic of this course is a recommender system. The course covers the essential of recommender systems such as recommendation techniques (collaborative, content-based, knowledge-based and hybrid) and evaluation methods, as well as exploring some further issues such as security issues, online market decision making process, recommendation systems in next generation web or IoT/ubiquitous environment. Also, this course intend that students catch up the recent research issues related to recommender systems. Extensive paper reading/presentation assignments and a project related to recommender systems will be issued.
Instructor
Kyungbaek Kim
Office : Engineering Building #6, 715
Tel : +82-62-530-3438
Email : kyungbaekkim@jnu.ac.kr
Office Hours : Wed 11:00 ~ 12:00
Time and Location
Tuesday 9:00-12:00, Engineering Building #6, 106
Textbook
- Recommender Systems : An Introduction, by Dietmar Jnnach, Markus Zanker, Alexander Felfering, and Gerhard Friedrich
Grading Policy
- Attendance : 10%
- Reading Assignments and exercises : 30%
- Tentatively Two papers per week : 13 papers
- Hadoop exercises
- Projects : 30%
- Personal Project : research on Hadoop related projects
- Team Project
- Exam : 30%
Lecture Notes
Lecture notes are accessible through the eClass of JNU portal.
- Syllabus
- 1.Introduction
- 2.Collaborative recommendation
- 3.Content-based recommendation
- 4.Knowledge-based recommendation
- 5.Hybrid recommendation approaches
Homeworks, Quiz, Midterm/Final Exam
All of the materials related to homeworks, quiz, midterm exam and final exam, including solutions, are accessible through the eClass of JNU portal.