EIT Digital

Basic Recommender Systems

Taught in English

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3,252 already enrolled


Gain insight into a topic and learn the fundamentals

Paolo Cremonesi

Instructor: Paolo Cremonesi


(41 reviews)

Intermediate level

Recommended experience

11 hours to complete
3 weeks at 3 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • 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.

Details to know

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There are 4 modules in this course

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.

What's included

11 videos2 readings1 quiz1 peer review2 discussion prompts

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.

What's included

12 videos1 quiz1 peer review2 discussion prompts

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.

What's included

9 videos1 quiz1 peer review2 discussion prompts

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.

What's included

9 videos1 quiz1 peer review2 discussion prompts


Instructor ratings
4.2 (7 ratings)
Paolo Cremonesi
EIT Digital
2 Courses5,225 learners

Offered by

EIT Digital

Recommended if you're interested in Data Analysis

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Reviewed on Oct 24, 2020

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