Recommender Systems (RecSys) are ubiquitous and have become inseparable parts of our everyday lives.
They are central to our experience with the digital world in everything we do and every service we consume providing
us with personalized experiences. They help us find our favourite items to purchase, our friends on social networks,
our favourite movies to watch, music to listen to, and books to read. Nowadays, RecSys have numerous applications that
transcend these classic taste-driven domains of social media and entertainment and are revolutionizing several sectors.
Education, industry, healthcare, tourism, transport and logistics are among the areas where the applications of RecSys
have gained momentum.
This lecture provides a comprehensive introduction to the computational methods used in designing and building real world
recommender systems. In this lecture, students will learn fundamental concepts, state-of-the-art tools and algorithms used to design,
build and evaluate RecSys engines. Students will also gain hands-on experience implementing these methods on selected case studies,
developing practical skills.
