Teaching plan for the course unit

 

Close imatge de maquetació

 

Print

 

General information

 

Course unit name: Recommenders

Course unit code: 572670

Academic year: 2018-2019

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 learning activities

75

 

-  Lecture with practical component

 

30

 

-  Document study

 

45

 

 

Competences to be gained during study

 

CB6 - Poseer y comprender conocimientos que aporten una base u oportunidad de ser originales en el desarrollo y/o aplicación de ideas, a menudo en un contexto de investigación

 

 

CB7 - Que los estudiantes sepan aplicar los conocimientos adquiridos y su capacidad de resolución de problemas en entornos nuevos o poco conocidos dentro de contextos más amplios (o multidisciplinares) relacionados con su área de estudio

 

CB9 - Que los estudiantes sepan comunicar sus conclusiones y los conocimientos y razones últimas que las sustentan¿ a públicos especializados y no especializados de un modo claro y sin ambigüedades

 

CE6 - Que los estudiantes sepan aplicar de forma efectiva herramientas analíticas y predictivas de aprendizaje automático.

 

CE7 - Que los estudiantes sepan entender, desarrollar y modificar los algoritmos analíticos y exploratorios que trabajan sobre conjuntos de datos. y aplicar el pensamiento crítico en estas tareas.

 

 

 

 

Learning objectives

 

Referring to knowledge

Understand the taxnonomy 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. Content-Based Recommneder Systems

*  Content-Based Recommneder Systems

4. Collaborative Recommender Systems

*  Collaborative Recommender Systems

5. Evaluation Metrics

*  Offline and Online measure to measure the performance of recommender systems. 

A/B testing

6. Item-Based Recommender Systems

*  Item-Based Recommender Systems

7. Dimensionality Reducction for Recommender Systems

*  Singular Value Decomposition; Probabilistic Matrix Factorization

 

8. Tricks & Problems

*  Learning to Rank

Context Aware Recommender Systems

Cold Start

Current Practices in Industry and research

 

 

Teaching methods and general organization

 

Oral presentation of the content in combination with practical sesions 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