K3: To know fundamental contents in their area of study starting from the basis of general secondary education and reaching a level proper of advanced textbooks, including also some aspects of the forefront of their field of study. S1: To plan and organize team work making the right decisions based on available information and gathering data in digital environments. S3: Ability to solve technological, computer, mathematical and statistical problems that may arise in data engineering and science, applying knowledge of mathematics, probability and statistics, programming, databases, and languages, grammars and automata. S4: Ability to solve mathematically formulated problems applied to various subjects, using numerical algorithms and computational techniques, and applying knowledge of: algebra; geometry; differential and integral calculus; numerical methods; numerical algorithms; statistics and optimization S5: Ability to correctly identify predictive problems corresponding to certain objectives and data, based on knowledge of algorithms, modeling, prediction and filtering, and to use the basic results of regression analysis as the basis for prediction methods S6: Ability to correctly identify classification problems corresponding to certain objectives and data, based on knowledge of algorithms, modeling, prediction and filtering, and to use the basic results of multivariate analysis as the basis for classification, clustering and dimension reduction methods S7: Capability for mathematical modeling, algorithmic implementation and optimization problem solving related to data science, relying on knowledge of mathematics, algorithms, programming and optimization. S9: Apply, design, develop, critically analyze and evaluate machine learning methods in classification, regression and clustering problems and for supervised, unsupervised and reinforcement learning tasks. S10: Apply, design, develop, critically analyze and evaluate solutions based on artificial neural networks S11: Apply, design, develop, critically analyze and evaluate solutions based on machine learning for applications in specific domains such as recommendation systems, natural language processing, Web or social networks S16: Ability to synthesize the conclusions obtained from the analyses carried out and present them clearly and convincingly both in writing and orally to both specialized and non-specialized audiences

1. Introduction to recommender systems. Type of data for the design of a recommender system, classification of the different methods and metrics for its evaluation. 2. Popularity-based, content-based and/or user-based recommender systems. 3. Collaborative filtering: neighborhood models and latent-based models. 4. Deep recommender systems: Neutral Collaborative Filtering (NFC) and extensions.