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Recommender Systems (19CBA11009)

General information

Type:

OP

Curs:

1

Period:

S semester

ECTS Credits:

3 ECTS

Teaching Staff:

Group Teacher Department Language
Marc Torrens Arnal Operaciones, Innovación y Data Sciences ENG

Prerequisites

None

Previous Knowledge

- Basic Pyhton programming skills

COURSE CONTRIBUTION TO PROGRAM

Recommender Systems are probably the most commercial application of Machine Learning. This course is about learning the main algorithm families to build scalable and data-driven recommender systems for commercial applications such as electronic commerce.

Course Learning Objectives

The main objective of the course is to learn the main algorithms and techniques to develop Recommender Systems that are widely used in the Internet industry. Students will learn the main aspects surrounding Recommender Systems including algorithms, evaluation, implementation, and business implications.

CONTENT

1. Introduction to Recommender Systems

2. Recommendation Methods

3. Collaborative Filtering

4. Content-based filtering

5. Building a recommendation engine in the real world

6. Final project preparation

7. Final project presentation

Methodology


Assessment criteria

The course will be evaluated as follows:

- 20% class attendance and participation
- 40% group final project presentation
- 40% individual exercises

Bibliography

- Recommender Systems Handbook. Lior Rokach, Bracha Shapira, Francesco Ricci.
- Coursera: Recommender Systems Specialization. University of Minnesota.

Timetable and sections

Group Teacher Department
Marc Torrens Arnal Operaciones, Innovación y Data Sciences

Timetable