Teaching plan for the course unit

 

 

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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

 

 

Estimated learning time

Total number of hours 75

 

Face-to-face and/or online activities

75

 

-  Lecture with practical component

Face-to-face

 

30

 

-  Document study

Face-to-face

 

45

 

 

Competences to be gained during study

 

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

 

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

 

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

 

CE6 - That students know how to effectively apply analytical and predictive tools of machine learning.

 

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.

 

 

 

 

Learning objectives

 

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

 

 

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

 

 

Teaching methods and general organization

 

Oral presentation of the content in combination with practical sessions associated to the different lectures of the course.

 

 

Official assessment of learning outcomes

 

50% of the final score: reports and code associated to the different practical sessions of the course
50% of the final score: final course exam

 

Examination-based assessment

50% of the final score: reports and code associated to the different practical sessions of the course
50% of the final score: final course exam