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Basic Recommender Systems(으)로 돌아가기

EIT 디지털 의 Basic Recommender Systems 학습자 리뷰 및 피드백

4.4
별점
31개의 평가

강좌 소개

This course introduces you to the leading approaches in recommender systems. The techniques described touch both collaborative and content-based approaches and include the most important algorithms used to provide recommendations. You'll learn how they work, how to use and how to evaluate them, pointing out benefits and limits of different recommender system alternatives. After completing this course, you'll be able to describe the requirements and objectives of recommender systems based on different application domains. You'll know how to distinguish recommender systems according to their input data, their internal working mechanisms, and their goals. You’ll have the tools to measure the quality of a recommender system and to incrementally improve it with the design of new algorithms. You'll learn as well how to design recommender systems tailored for new application domains, also considering surrounding social and ethical issues such as identity, privacy, and manipulation. Providing affordable, personalised and high-quality recommendations is always a challenge! This course also leverages two important EIT Overarching Learning Outcomes (OLOs), related to creativity and innovation skills. In trying to design a new recommender system you need to think beyond boundaries and try to figure out how you can improve the quality of the predictions. You should also be able to use knowledge, ideas and technology to create new or significantly improved recommendation tools to support choice-making processes and strategies in different and innovative scenarios, for a better quality of life....

최상위 리뷰

필터링 기준:

Basic Recommender Systems의 5개 리뷰 중 1~5

교육 기관: Adrien B

2021년 6월 14일

교육 기관: Amber A

2021년 4월 13일

교육 기관: Jbene M

2021년 1월 1일

교육 기관: Ruslan T

2020년 10월 25일

교육 기관: Ravikumar N

2021년 12월 9일