ECTS - Introduction to Recommender Systems

Atılım University - ECTS INFORMATION GUIDE

Introduction to Recommender Systems (CMPE555) Course Detail

Course Name Course Code Season Lecture Hours Application Hours Lab Hours Credit ECTS
Introduction to Recommender Systems CMPE555 Area Elective 3 0 0 3 5
Pre-requisite Course(s)
N/A
Course Language English
Course Type Elective Courses
Course Level Natural & Applied Sciences Master's Degree
Mode of Delivery Face To Face
Learning and Teaching Strategies Lecture, Drill and Practice, Problem Solving.
Course Coordinator
Course Lecturer(s)
Course Assistants
Course Objectives This course, which is designed to serve as the first course in the Recommender Systems specialization, introduces the concept of recommender systems, reviews several examples in detail. The course includes topics of collaborative filtering algorithms, content-based recommendation algorithms and hybrid recommendation algorithms development, explanations and evaluation metrics in recommender systems. Furthermore, the course provides students capability to implement evaluation techniques of recommender systems, and implement robustness and privacy protection techniques for recommender systems.
Course Learning Outcomes The students who succeeded in this course;
  • Learn and create essential components of recommender systems
  • Applies collaborative filtering algorithms, content-based recommendation algorithms and hybrid recommendation algorithms
  • Describes experiments for evaluating recommender systems and evaluates experiment results.
  • Designs experiments for evaluating robustness of recommender systems
  • Designs experiments for evaluating privacy of recommender systems
Course Content Basic Concepts of recommender systems, collaborative filtering algorithms, content-based recommendation algorithms, knowledge-based recommendation algorithms, and hybrid recommendation algorithms, evaluating recommender systems, a case study to generate personalized recommendations.

Weekly Subjects and Releated Preparation Studies

Week Subjects Preparation
1 Introduction Chapter 1
2 Introduction into Basic Concepts Chapter 1
3 Collaborative Recommendation Chapter 2
4 Collaborative Recommendation Chapter 2
5 Content-Based Recommendation Chapter 3
6 Content-Based Recommendation Chapter 3
7 Knowledge-Based Recommendation Chapter 4
8 Hybrid Recommendation Approaches Chapter 5
9 Explanations in Recommender Systems Chapter 6
10 Evaluating Recommender Systems Chapter 7
11 Evaluating Recommender Systems Chapter 7
12 Case Study - Personalized Recommendations Chapter 8
13 Case Study - Personalized Recommendations Chapter 8
14 Attacks on Collaborative Recommender Systems Chapter 9

Sources

Course Book 1. Jannach, D., Zanker, M., Felfernig, A., & Friedrich, G. (2010). Recommender Systems: An Introduction. Cambridge University Press. www.recommen derbook.net
Other Sources 2. Aggarwal, C. C. (2016). Recommender systems (Vol. 1). Cham: Springer International Publishing.
3. Ricci, F., Rokach, L., & Shapira, B. (2011). Introduction to recommender systems handbook. In Recommender systems handbook (pp. 1-35). Springer, Boston, MA.
4. Yoo, K. H., Gretzel, U., & Zanker, M. (2012). Persuasive recommender systems: conceptual background and implications. Springer Science & Business Media.
5. Introduction to Information Retrieval, Cambridge University Press. 2008 http://nlp.stanford.edu/IR-book/

Evaluation System

Requirements Number Percentage of Grade
Attendance/Participation - -
Laboratory - -
Application - -
Field Work - -
Special Course Internship - -
Quizzes/Studio Critics - -
Homework Assignments 3 20
Presentation - -
Project - -
Report - -
Seminar - -
Midterms Exams/Midterms Jury 2 40
Final Exam/Final Jury 1 40
Toplam 6 100
Percentage of Semester Work
Percentage of Final Work 100
Total 100

Course Category

Core Courses X
Major Area Courses
Supportive Courses
Media and Managment Skills Courses
Transferable Skill Courses

The Relation Between Course Learning Competencies and Program Qualifications

# Program Qualifications / Competencies Level of Contribution
1 2 3 4 5
1 An ability to apply advanced knowledge of computing and/or informatics to solve software engineering problems. X
2 Develop solutions using different technologies, software architectures and life-cycle approaches.
3 An ability to design, implement and evaluate a software system, component, process or program by using modern techniques and engineering tools required for software engineering practices.
4 An ability to gather/acquire, analyze, interpret data and make decisions to understand software requirements.
5 Skills of effective oral and written communication and critical thinking about a wide range of issues arising in the context of working constructively on software projects.
6 An ability to access information in order to follow recent developments in science and technology and to perform scientific research or implement a project in the software engineering domain.
7 An understanding of professional, legal, ethical and social issues and responsibilities related to Software Engineering.
8 Skills in project and risk management, awareness about importance of entrepreneurship, innovation and long-term development, and recognition of international standards of excellence for software engineering practices standards and methodologies.
9 An understanding about the impact of Software Engineering solutions in a global, environmental, societal and legal context while making decisions.
10 Promote the development, adoption and sustained use of standards of excellence for software engineering practices.

ECTS/Workload Table

Activities Number Duration (Hours) Total Workload
Course Hours (Including Exam Week: 16 x Total Hours) 16 3 48
Laboratory
Application
Special Course Internship
Field Work
Study Hours Out of Class 16 2 32
Presentation/Seminar Prepration
Project
Report
Homework Assignments 3 4 12
Quizzes/Studio Critics
Prepration of Midterm Exams/Midterm Jury 2 5 10
Prepration of Final Exams/Final Jury 1 10 10
Total Workload 112