Course - Recommender Systems - TDT4215
Recommender Systems
Choose study yearAbout
About the course
Course content
- Content-based Filtering
- Collaborative Filtering
- Exploration vs. Exploitation
- Evaluation Methodology and Metrics Personalization
- Context-awareness
- Natural Language Processing
- Ethical Considerations
- Generative AI
- Multi-objective Recommendation
Learning outcome
Knowledge: Recommendation algorithms, including content-based filtering and collaborative filtering. Evaluation methodology and a variety of metrics. Critical reflection of ethical aspects. Considering contextual information. Feature engineering with natural language processing technology. Optimization for multiple criteria.
Skills: Statistical methods to analyze the output of recommender systems. Implementing scalable systems.
General competence: Critical thinking. Teamwork. Problem solving.
Learning methods and activities
Lectures, exercises and group project.
The course is taught in English.
Compulsory assignments
- Group work
Further on evaluation
Students must pass the obligatory group project to be able to take the exam.
The text for the written final exam will be in English.
The candidates may choose to write their answers in either English or Norwegian.
If there is a re-sit examination, the examination form may change from written to oral.
Recommended previous knowledge
Course TDT4145 Data modelling and database systems, or equivalent.
Basic knowledge of machine learning.
Course materials
Announced at start of semester.
Credit reductions
| Course code | Reduction | From |
|---|---|---|
| TDT4215 | 3.7 sp | Autumn 2011 |
| TDT4215 | 3.7 sp | Autumn 2011 |
Subject areas
- Informatics
- Technological subjects