Recommender Systems

This course on recommender systems delves into the fundamental concepts that shape modern digital interactions and decision-making. By analyzing how recommendation algorithms are used in platforms like streaming services, ecommerce, and social media, students will understand the mechanisms that influence user engagement and behavior. The course explores the development and application of collaborative filtering, content-based recommender, and hybrid models to predict and recommend personalized content. Additionally, it emphasizes practical methodologies for conducting offline experimentation and measuring the effectiveness of these systems, ensuring that students gain a comprehensive view of the evaluation process. The course will also take a critical look at the challenges and societal issues inherent in creating and maintaining recommender systems. Topics include data sparsity, algorithmic bias, and the need for transparency in model decisions. Students will work on practical projects using popular tools and frameworks, gaining experience in data preprocessing, model training, and model evaluation. The course further emphasizes the critical analysis of recommendation outcomes. students will learn to analyze the recommendation results, discussing potential ethical and societal implications. This includes reflecting on how recommender systems can influence user behavior and societal norms, highlighting issues such as filter bubbles and content diversity. By the end of the course, participants will have developed the skills to design, implement, evaluate, and analyze recommender systems.

The final grade of the course consists of the following components:

• Individual Presentation (weighting 30%)

• Group Report (weighting 70%)

Final grade calculation = 0.3 * Individual Presentation + 0.7 * Group Report

A passing final grade for the course can only be earned when for all components at least a 5.0 is earned, and the weighted final grade is at least a 5.8.

In case of an insufficient final result, repair options may exist in accordance with Article 17A, Times and number of examinations, sub 1, of the Teaching and Examination Regulations, for:

• Individual Presentation: Repair opportunity

• Group Report: Repair opportunity

Disclaimer: information may change depending on unforeseen circumstances or measures (see: TER Art 2, sub 5).