ECTS - Introduction to Recommender Systems
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 | Ph.D. |
| Mode of Delivery | Face To Face |
| Learning and Teaching Strategies | Lecture, Drill and Practice, Problem Solving. |
| Course Lecturer(s) |
|
| 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;
|
| 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 | Develops the ability to apply advanced knowledge of mathematics, science, and engineering to the analysis, design, and optimization of complex systems. | |||||
| 2 | Implements long-term research and development studies in the major fields of Electrical and Electronics Engineering. | |||||
| 3 | Use modern engineering tools, techniques and facilities in design and other engineering applications. | X | ||||
| 4 | Does research actively on innovation and entrepreneurship. | |||||
| 5 | Develops the ability to effectively communicate and present research outcomes. | |||||
| 6 | Keeps up with recent advancements in science and technology and effectively accesses relevant information. | |||||
| 7 | Will have professional and ethical responsibility. | |||||
| 8 | Develops ability to effectively communications in both Turkish and English. | |||||
| 9 | Develops ability on project management. | |||||
| 10 | Develops the ability to work successfully at project teams in interdisciplinary fields. | X | ||||
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 | ||