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.
von
EIT Digital
Politecnico di Milano
Basic Recommender Systems
EIT DigitalÜber diesen Kurs
2.964 kürzliche Aufrufe
Flexible Fristen
Setzen Sie Fristen gemäß Ihrem Zeitplan zurück.
Zertifikat zur Vorlage
Erhalten Sie nach Abschluss ein Zertifikat
100 % online
Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.
Stufe „Mittel“
Basic notions of linear algebra
Ca. 12 Stunden zum Abschließen
Englisch
Untertitel: Englisch
Was Sie lernen werden
You'll be able to build a basic recommender system.
You'll be able to choose the family of recommender systems that best suits the kind of input data, goals and needs.
You'll learn how to identify the correct evaluation activities to measure the quality of a recommender system, based on goals and needs.
You'll be able to point out benefits and limits of different techniques for recommender systems in different scenarios.
Flexible Fristen
Setzen Sie Fristen gemäß Ihrem Zeitplan zurück.
Zertifikat zur Vorlage
Erhalten Sie nach Abschluss ein Zertifikat
100 % online
Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.
Stufe „Mittel“
Basic notions of linear algebra
Ca. 12 Stunden zum Abschließen
Englisch
Untertitel: Englisch
von
EIT Digital
EIT Digital is a European education and innovation organisation with a mission to foster digital technology innovation and entrepreneurial talent for economic growth and quality of life. By linking education, research, and business, EIT Digital empowers digital top talent for the future.
Politecnico di Milano
Politecnico di Milano is a scientific-technological University, which trains engineers, architects and industrial designers.
Lehrplan - Was Sie in diesem Kurs lernen werden
3 Stunden zum Abschließen
BASIC CONCEPTS
In this first module, we'll review the basic concepts for recommender systems in order to classify and analyse different families of algorithms, related to specific set of input data. At the end, you’ll be able to choose the most suitable type of algorithm based on the data available, your needs and goals. Conversely, you'll know how to select the input data based on the algorithm you want to use.
3 Stunden zum Abschließen
11 Videos (Gesamt 29 min), 2 Lektüren, 2 Quiz
3 Stunden zum Abschließen
EVALUATION OF RECOMMENDER SYSTEMS
In this second module, we'll learn how to define and measure the quality of a recommender system. We'll review different metrics that can be used to measure for this purpose. At the end of the module you'll be able to identify the correct evaluation activities required to measure the quality of a given recommender system, based on goals and needs.
3 Stunden zum Abschließen
12 Videos (Gesamt 35 min)
3 Stunden zum Abschließen
CONTENT-BASED FILTERING
In this module we’ll analyse content-based recommender techniques. These algorithms recommend items similar to the ones a user liked in the past. We’ll review different similarity functions and you’ll then be able to choose the more suitable one for your system. The main input is the Item-Content Matrix (ICM) which describes all the attributes for each item. We’ll see how we can improve the quality of content-based techniques, by normalising and tuning the importance of each attribute in the ICM: you’ll be able to use some specific tuning strategies in order to obtain the best quality recommendations from your system. So, at the end of this module, you’ll know how to build a content-based recommender system, how to clean and normalize your input data.
3 Stunden zum Abschließen
9 Videos (Gesamt 19 min)
3 Stunden zum Abschließen
COLLABORATIVE FILTERING
In this module we’ll study collaborative filtering techniques, which use the User Rating Matrix (URM) as the main input data, describing the interaction between users and items. We’ll learn how to build non-personalised recommender systems and how to normalise the URM, in order to provide better recommendations. At the end of the module you’ll be able to select the most appropriate similarity function and the most suitable way to compute similarity, overcoming issues related to explicit ratings.
3 Stunden zum Abschließen
9 Videos (Gesamt 42 min)
Bewertungen
Top-Bewertungen von BASIC RECOMMENDER SYSTEMS
von RT24. Okt. 2020
There is a nice introduction to recommender systems field
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