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Our Degree Programs
Establishment
Language of instruction
English
Teaching content
MARKETING
Training officer(s)
S.GEUENS
Stakeholder(s)
Stijn GEUENS
Présentation
Prerequisite
Students should have a basic knowledge of the R programming language.
Basic knowledge of matrix algebra.
Basic knowledge of matrix algebra.
Goal
At the end of the course, the student should be able to:
- understand the basics of recommendation tools
- distinguish different recommendation algorithms including their advantages and disadvantages
- create simple recommendation algorithms
- evaluate and compare recommendation tools
- build a REST API to communicate with recommendation systems
- know current hot topics in recommendation tools
These competencies and/or skills contribute to the following learning objectives
- 2.B Solve professional dilemmas using concepts of CSR and ethics
- 3.C Organize change management processes
- 5.C Employ state-of-the-art management techniques
- 7.C Effectively apply in-depth specialized knowledge to take advantage of contemporary opportunities in their professional field
- understand the basics of recommendation tools
- distinguish different recommendation algorithms including their advantages and disadvantages
- create simple recommendation algorithms
- evaluate and compare recommendation tools
- build a REST API to communicate with recommendation systems
- know current hot topics in recommendation tools
These competencies and/or skills contribute to the following learning objectives
- 2.B Solve professional dilemmas using concepts of CSR and ethics
- 3.C Organize change management processes
- 5.C Employ state-of-the-art management techniques
- 7.C Effectively apply in-depth specialized knowledge to take advantage of contemporary opportunities in their professional field
Presentation
• Introduction to recommendation tools.
• Non-personalized recommendation tools.
• Collaborative filtering (theory and hands-on programming).
• Content-based recommendation tools (theory and hands-on programming).
• Hybrid recommendation tools (theory and hands-on programming).
• Evaluating recommendation tools (theory and hands-on programming).
• Current topics in recommendation tools research.
• Non-personalized recommendation tools.
• Collaborative filtering (theory and hands-on programming).
• Content-based recommendation tools (theory and hands-on programming).
• Hybrid recommendation tools (theory and hands-on programming).
• Evaluating recommendation tools (theory and hands-on programming).
• Current topics in recommendation tools research.
Modalités
Organization
| Type | Amount of time | Comment | |
|---|---|---|---|
| Face to face | |||
| Interactive class | 16,00 | ||
| Independent study | |||
| Group Project | 18,00 | ||
| Individual Project | 10,00 | ||
| Independent work | |||
| Research | 6,00 | ||
| Overall student workload | 50,00 | ||
Evaluation
Students will be assessed based on their participation in class, an individual project, and a group project.
| Control type | Duration | Amount | Weighting |
|---|---|---|---|
| Others | |||
| Group Project | 18,00 | 1 | 40,00 |
| Individual Project | 10,00 | 0 | 25,00 |
| Continuous assessment | |||
| Participation | 16,00 | 1 | 35,00 |
| TOTAL | 100,00 | ||
Ressources
Bibliography
Recommender Systems: An Introduction by Dietmar Jannach, Markus Zanker, Alexander Felfernig, Gerhard Friedrich ; 2010 ; ISBN-10: 0521493366; Cambridge University Press -
Ricci, F., Rokach, L., Shapira, B., Kantor P.B. (2011). Recommendation Systems Handbook. Springer: NewYork, ISBN 978-0-387-85819-7 -
Herlocker, J. L., Konstan, J. A., Terveen, K., & Riedl, J. T. (2004). Evaluating Collaborative Filtering Recommender Systems. ACM Transactions on Information Systems, 22(1), 5-53. -
Ricci, F., Rokach, L., Shapira, B., Kantor P.B. (2011). Recommendation Systems Handbook. Springer: NewYork, ISBN 978-0-387-85819-7 -
Herlocker, J. L., Konstan, J. A., Terveen, K., & Riedl, J. T. (2004). Evaluating Collaborative Filtering Recommender Systems. ACM Transactions on Information Systems, 22(1), 5-53. -
Review
Bobadilla, J., Ortega, F., Hernando, A., & Gutierrez, A. (2013). Recommender systems survey. Knowledge-Based Systems, 46, 109-132. -Review
Adomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6), 734-749. -Review
Burke, R. (2002). Hybrid Recommender Systems: Survey and Experiments. User Modeling and User-Adapted Interaction, 12(4), 331-370. -Review
Internet resources