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Advanced Recommender Systems

Learners:

Instructor: Paolo Cremonesi

Duration:

This Advanced Recommender Systems course is the perfect opportunity to learn how to use advanced machine learning techniques to build more sophisticated recommender systems. You will learn how to manage hybrid information, combine different filtering techniques, use factorization machines, identify new trends and challenges, and create new or significantly improved recommendation tools. With this course, you will be able to design more sophisticated recommender systems and solve the cross-domain recommendation problem. Take advantage of this opportunity to develop your creativity and innovation skills and create new or significantly improved recommendation tools.▼▲

Course Feature Course Overview Course Provider

Go to class

Course Feature

Cost:

Free

Provider:

Coursera

Certificate:

Paid Certification

Language:

English

Start Date:

22nd May, 2023

Course Overview

❗The content presented here is sourced directly from Coursera platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.

Updated in [July 27th, 2023]

In this Advanced Recommender Systems course, students will learn how to use advanced machine learning techniques to build more sophisticated recommender systems. Machine Learning is able to provide recommendations and make better predictions, by taking advantage of historical opinions from users and building up the model automatically, without the need for manual input. At the end of the course, students will be able to manage hybrid information, combine different filtering techniques, use factorization machines, design more sophisticated recommender systems, solve the cross-domain recommendation problem, identify new trends and challenges in providing recommendations, and create new or significantly improved recommendation tools to support choice-making processes and solve real-life problems in complex and innovative scenarios. This course leverages two important EIT Digital Overarching Learning Outcomes (OLOs), related to creativity and innovation skills.