Basic Recommender Systems

4.3
stars
14 ratings
•
2 reviews

Paolo Cremonesi

Offered By

EIT Digital

Politecnico di Milano

Basic Recommender Systems

EIT Digital

About this Course

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.

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Earn a Certificate upon completion

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Flexible deadlines

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Intermediate Level

Basic notions of linear algebra

Approx. 12 hours to complete

English

Subtitles: English

What you will 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.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Intermediate Level

Basic notions of linear algebra

Approx. 12 hours to complete

English

Subtitles: English

Instructor

Instructor rating

4.75/5 (5 Ratings)

Paolo Cremonesi

Associate Professor

Department of Electronics, Information and Bioengineering - Politecnico di Milano

833 Learners

2 Courses

Offered by

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 talents for the future.

EIT Digital provides online and blended Innovation and Entrepreneurship education to raise quality, increase diversity and availability of the top-level content provided by 20 leading technical universities around Europe. The universities deliver a unique blend of the best of technical excellence and entrepreneurial skills and mindset to digital engineers and entrepreneurs at all stages of their careers. The academic partners support Coursera’s bold vision to enable anyone, anywhere, to transform their lives by accessing the world’s best learning experience. This means that EIT Digital gradually shares parts of its entrepreneurial and academic education programmes to demonstrate its excellence and make it accessible to a much wider audience. EIT Digital’s online education portfolio can be used as part of blended education settings, in both Master and Doctorate programmes, and for professionals as a way to update their knowledge. EIT Digital offers an online programme in 'Internet of Things through Embedded Systems'. Achieving all certificates of the online courses and the specialization provides an opportunity to enroll in the on campus program and get a double degree. Please visit https://www.eitdigital.eu/eit-digital-academy/

Politecnico di Milano

Politecnico di Milano is a scientific-technological University, which trains engineers, architects and industrial designers.

Syllabus - What you will learn from this course

Week

Week 1

3 hours to complete

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 hours to complete

11 videos (Total 29 min), 2 readings, 2 quizzes

11 videos

Course overview and welcome by the instructor2m

Welcome by the instructor - module overview1m

Introduction to Recommender Systems3m

Taxonomy of Recommender Systems6m

Item-Content Matrix1m

User-Rating Matrix2m

Inferring Preferences3m

Recap by the instructor1m

Non-personalized Recommender Systems3m

Global Effects2m

Conclusions by the instructor1m

2 readings

Course Syllabus10m

Credits & Acknowledgements5m

1 practice exercise

Module 1 - Graded Assessment45m

Week

Week 2

3 hours to complete

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 hours to complete

12 videos (Total 35 min)

Week

Week 3

3 hours to complete

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 hours to complete

9 videos (Total 19 min)

Week

Week 4

3 hours to complete

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 hours to complete

9 videos (Total 42 min)

Reviews

4.3

2 reviews

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TOP REVIEWS FROM BASIC RECOMMENDER SYSTEMS

by RTOct 24, 2020

There is a nice introduction to recommender systems field

View all reviews

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Basic Recommender Systems

Basic Recommender Systems