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Presentation
Presentation
This module provides the foundations for designing and implementing recommendation systems (RS). Students learn about different relevant algorithms, including collaborative filtering, which involves predicting user preferences based on similar users' preferences, and content-based recommendation. In addition to these fundamental topics, students also learn more advanced techniques such as hybrid RS, context-aware RS, using deep learning, and RS evaluation. The course has a theoretical-practical approach that includes implementing RS using real-world datasets, to gain practical experience with cutting-edge techniques. At the end of the course, students will have a deep understanding of the state-of-the-art in recommendation systems and will be able to apply these techniques to real-world problems.
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Class from course
Class from course
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Degree | Semesters | ECTS
Degree | Semesters | ECTS
Master Degree | Semestral | 7
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Year | Nature | Language
Year | Nature | Language
2 | Optional | Português
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Code
Code
ULHT6347-23278
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Prerequisites and corequisites
Prerequisites and corequisites
Not applicable
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Professional Internship
Professional Internship
Não
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Syllabus
Syllabus
- S1. Introduction to Recommender Systems
- S2. Collaborative Filtering
- S3. Content-Based Filtering
- S4. Hybrid Recommender Systems
- S5. Evaluation Metrics for Recommender Systems
- S6. Matrix Factorization
- S7. Deep Learning Approaches to Recommender Systems
- S8. Ethics and Social Implications of Recommender Systems
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Objectives
Objectives
The the learning goals of the complements of recommendation systems module are:
- LG1. Understanding the fundamentals of recommendation systems, including their main challenges and applications
- LG2. Understanding and comparing different types of recommendation algorithms, including collaborative modelling, content-based, and hybrid
- LG3. Understanding the different evaluation metrics of recommendation systems and be able to evaluate the quality of a recommendation system
- LG4. Being able to design and implement a recommendation system, choosing the appropriate algorithm and adjusting parameters to achieve optimal performance
- LG5. Understanding the ethical and privacy issues involved in the implementation of recommendation systems and be aware of the social and economic implications of these systems
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Teaching methodologies and assessment
Teaching methodologies and assessment
The module consists of lectures, practical sessions, and group projects. The lectures provide an overview of the concepts and techniques used in recommender systems, while the practical sessions and group projects give students hands-on experience designing and implementing these systems.
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References
References
- Falk, K. (2019). Practical recommender systems. Simon and Schuster.
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Office Hours
Office Hours
A definir no início do semestre
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Mobility
Mobility
No
