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 Gains the ability to apply advanced computing and/or information knowledge in solving software engineering problems. X
2 Develops solutions using different technologies, software architectures and life-cycle approaches.
3 Gains the ability to design, implement, and evaluate a software system, component, process, or program using modern techniques and engineering tools for software engineering practices.
4 Gains ability to gather/acquire, analyze, interpret data and make decisions to understand software requirements.
5 Gains 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 Gains the ability to access information to follow current developments in science and technology, conducts scientific research in the field of software engineering, and conducts a project.
7 Acquires an understanding of professional, legal, ethical and social issues and responsibilities related to Software Engineering.
8 Acquires project and risk management skills and gains awareness of the importance of entrepreneurship, innovation, and sustainable development, as well as international standards and methodologies.
9 Understands the impact of Software Engineering solutions in a global, environmental, societal and legal context while making decisions.
10 Gains awareness of the development, adoption, and ongoing support for the use of excellence standards in 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