CourseSpecifications
Valid as from the academic year 2024-2025

Recommender Systems (E018230)

Course size
Credits 6.0(nominal values; actual values may depend on programme)
Study time 180 h

Course offerings and teaching methods in academic year 2024-2025
A (semester 2)
EnglishGent

Lecturers in academic year 2024-2025
De Pessemier, Toon
Martens, Luc

lecture

TW05
TW05

Offered in the following programmes in 2024-2025
Master of Science in Industrial Engineering and Operations Research(main subject
Manufacturing and Supply Chain Engineering)
Master of Science in Industrial Engineering and Operations Research(main subject
Transport and Mobility Engineering)
Master of Science in Computer Science Engineering
Master of Science in Computer Science Engineering
Master of Science in Industrial Engineering and Operations Research0.0h

lecturer-in-charge
co-lecturer
crdts
6offeringA6A666AAA

Teaching languages
EnglishKeywords
Personalization, profiling, self-learning systems, modelling user interests, data mining
Position of the course
Recommender systems are techniques and algorithms that generate content suggestions
based on personal interests of the user. These systems are typically used to cope with theproblem of information overload on many online services such as Amazon, Netflix andFacebook.
Research on recommender systems is a recent domain, originating from research on
information retrieval, machine learning and data mining. As with search engines, information orcontent will be retrieved (information retrieval) and presented to the end user. Unlike searchengines that use the entered search terms, recommender systems will select content based on
personal interests. A recommender system will automatically learn these personal interests(machine learning) based on the user behavior such as interactions with the service and any
feedback. Learning these personal preferences is performed using large amounts of historical
data in which specific patterns are identified (data mining).
Contents• Background and objectives of recommender systems for the various stakeholders (user,• service provider)• Input for recommender systems• • Processing implicit and explicit feedback• • Scaling and normalization of data
• • Data sparsity problems• • Unary data vs. rating data
• Output of recommender systems• • Predictions vs. recommendations• Social aspects(Approved)1• • The filter bubble• • Privacy aspects• Non-personalized recommender systems• • Product-association rules• • Popularity metrics• Recommendation algorithms• • Content-based recommender systems• • • Vector-Space model• • • Similarity metrics for content-based systems• • • Word2Vec• • Collaborative filtering systems• • • Items-based vs. user-based• • • Collective intelligence• • • Similarity metrics for collaborative filtering• • • Cold start and scalability problems• • • Restricted Boltzmann machines• • • Trust networks• Methods based on dimensionality reduction• • Singular Value Decomposition• • Funk SVD• Advanced recommendation techniques• • Algorithms based on social networking data
• • Rule-based systems and case-based reasoning• • Algorithms based on trust and reputation of users• • Combining algorithms in hybrid systems• • Recommendations using neural networks• Recommender systems in practice• • Recommendation architectures• • Implementation aspects• • Optimizing calculations• • Application domains• Evaluation of recommender systems• • Offline evaluation• • • Evaluation methodologies• • • Accuracy metrics• • • Decision-support metrics• • • Rank metrics• • Online evaluation• • • User evaluation• • • A/B testing• • Additional quality metrics• • • Serendipity• • • Diversity• • • Usefulness• • • Confidence• Explaining recommendations (explanations)• • Active learning for recommender systems• Context-aware recommender systems• • Context dimensions• • Context generalization• • Contextual pre-filtering, contextual post-filtering, contextual modelling• Recommendations for groups• • Aggregation of profiles vs. aggregation of recommendations• • Aggregation methods• Attacks on recommender systems• • Attack strategies• Dialog-based recommender systems• • Interaction between user and recommender system• • Expert-based systems
Initial competences
Basic knowledge of math and programming
Final competences(Approved)2

1 Modelling of user interests, interactions, and intentions.
2 Analysing and interpreting data originating from user interactions in the context of designing
1 and developing a recommender system.
3 Designing a recommender system focused on a specific case.
4 Formulating an evaluation methodology in terms of the goals of the recommender system.
5 Reporting and critically evaluating the results of a recommender system.
Conditions for credit contract
Access to this course unit via a credit contract is determined after successful competences assessment
Conditions for exam contract
This course unit cannot be taken via an exam contract
Teaching methods
LectureExtra information on the teaching methods• Lectures (hoorcolleges): teaching students the fundamentals of recommender systems.
• Independent work. Computer excercises: students gain practical knowledge about• recommender systems by specific assignments.
Study material
Type: Slides
Name: slides theory supplemented with notes
Indicative price: Free or paid by faculty
Optional: noLanguage : English
Number of Slides : 900
Oldest Usable Edition : 2023
Available on Ufora : Yes
Online Available : Yes
Available in the Library : No
Available through Student Association : No
Additional information: For the open book exam, students are asked to bring the printed slides
References• Recommender Systems - An Introduction, D. Jannach, M. Zanker, A. Felfernig, and G.
• Friedrich.
• Recommender Systems Handbook Second Edition, F. Ricci, L. Rokach, and B. Shapira.
Course content-related study coaching
The teacher(s) or his assistant(s) will be available during and in between lectures and computerexcercises for further explanation. The standard functionalities of the electronic learning
platform will also be used.
Assessment moments
end-of-term and continuous assessment
Examination methods in case of periodic assessment during the first examination period
Written assessment
Examination methods in case of periodic assessment during the second examination period
Written assessment
Examination methods in case of permanent assessment
Assignment
Possibilities of retake in case of permanent assessment
examination during the second examination period is possible
Extra information on the examination methods• Written exam: periodic evaluation, aimed at understanding and being able to apply the• course material.
• Computer excercises: permanent evaluation, aimed at being able to apply the theory in• practice. The students will implement some recommendation algorithms and evaluate and• interpret the results. The focus is on applying the knowledge gained in the lectures in practice• by implementing a recommender systems. The students will complete the full cycle of• deploying a recommender system: problem analysis, design, development, evaluation, and(Approved)3• interpretation of results.
Calculation of the examination mark• Written exam: 40%• Independent Work. Computer excercises: 60%
Facilities for Working Students
Timing of the computer excercises can be changed for working students.

(Approved)

4