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General information |
Course unit name: Recommenders
Course unit code: 572670
Academic year: 2021-2022
Coordinator: Santiago Segui Mesquida
Department: Department of Mathematics and Computer Science
Credits: 3
Single program: S
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Estimated learning time |
Total number of hours 75 |
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Face-to-face and/or online activities |
75 |
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- Lecture with practical component |
Face-to-face |
30 |
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- Document study |
Face-to-face |
45 |
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Competences to be gained during study |
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CB6 - Possess and understand knowledge that provides a basis or opportunity to be original in the development and / or application of ideas, often in a research context
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CB7 - That students know how to apply the knowledge acquired and their ability to solve problems in new or unfamiliar environments within broader (or multidisciplinary) contexts related to their area of study
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CB9 - That students know how to communicate their conclusions and the knowledge and ultimate reasons that support them to specialized and non-specialized audiences in a clear and unambiguous way
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CE6 - That students know how to effectively apply analytical and predictive tools of machine learning.
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CE7 - That the students know how to understand, develop and modify the analytical and exploratory algorithms that work on data sets. and apply critical thinking in these tasks.
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Learning objectives |
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Referring to knowledge Understand the taxonomy of Recommender Systems
Learn to apply a recommender system in different types of problems
Learn to evaluate the recommender systems
Develop and use different recommender system methods |
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Teaching blocks |
1. Introduction to Recommenders Systems
* Introduction to Recommenders Systems
2. Non Personalized Recommender Systems
* Non Personalized Recommender Systems
3. Collaborative Recommender Systems
* Collaborative Recommender Systems
4. Factorization Models
5. Content-Based Recommneder Systems
* Content-Based Recommneder Systems
6. Bias and Ethics in RecSys. Gender Discrimination
7. Factorization Machines
8. Learning to Rank
9. Deep Learning for Recommender Systems
10. Evaluation Metrics
* Offline and Online measure to measure the performance of recommender systems.
A/B testing
11. RecSys Challenge
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Teaching methods and general organization |
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Oral presentation of the content in combination with practical sessions associated to the different lectures of the course. |
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Official assessment of learning outcomes |
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50% of the final score: reports and code associated to the different practical sessions of the course
Examination-based assessment 50% of the final score: reports and code associated to the different practical sessions of the course |
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Reading and study resources |
Consulteu la disponibilitat a CERCABIB
Book
Aggarwal, Charu C. Recommender systems. New York [etc.] : Springer, 2016. 