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 | Natural & Applied Sciences Master's Degree |
| 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 | Applies knowledge of mathematics, science, and engineering | X | ||||
| 2 | Designs and conducts experiments, analyzes and interprets experimental results. | X | ||||
| 3 | Designs a system, component, or process to meet specified requirements. | X | ||||
| 4 | Works effectively in interdisciplinary fields. | |||||
| 5 | Identifies, formulates, and solves engineering problems. | X | ||||
| 6 | Has awareness of professional and ethical responsibility. | |||||
| 7 | Communicates effectively. | X | ||||
| 8 | Recognizes the need for lifelong learning and engages in it. | X | ||||
| 9 | Has knowledge of contemporary issues. | X | ||||
| 10 | Uses modern tools, techniques, and skills necessary for engineering applications. | X | ||||
| 11 | Has knowledge of project management skills and international standards and methodologies. | |||||
| 12 | Develops engineering products and prototypes for real-world problems. | X | ||||
| 13 | Contributes to professional knowledge. | X | ||||
| 14 | Conducts methodological and scientific research. | X | ||||
| 15 | Produces, reports, and presents a scientific work based on original or existing knowledge. | X | ||||
| 16 | Defends the original idea generated. | |||||
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 | ||