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Basic Recommender Systems

Basic Recommender Systems

Instructor: Paolo Cremonesi

3,851 already enrolled

Included with

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4 modules

Gain insight into a topic and learn the fundamentals.

43 reviews

Intermediate level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

43 reviews

Intermediate level

Recommended experience

1 week to complete

at 10 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.

Skills you'll gain

Details to know

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Assessments

4 assignments¹

AI Graded see disclaimer

Taught in English

91% of learners achieved a positive career outcome

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There are 4 modules in this course

The Basic Recommender Systems 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 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! The 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.

Module details

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 assignment1 peer review2 discussion prompts

11 videosTotal 29 minutes

Course overview and welcome by the instructor3 minutes
Welcome by the instructor - module overview2 minutes
Introduction to Recommender Systems4 minutes
Taxonomy of Recommender Systems6 minutes
Item-Content Matrix2 minutes
User-Rating Matrix3 minutes
Inferring Preferences3 minutes
Recap by the instructor1 minute
Non-personalized Recommender Systems3 minutes
Global Effects2 minutes
Conclusions by the instructor1 minute

2 readingsTotal 15 minutes

Course Syllabus10 minutes
Credits & Acknowledgements5 minutes

1 assignmentTotal 45 minutes

Module 1 - Graded Assessment45 minutes

1 peer reviewTotal 45 minutes

Differences between implicit and explicit ratings45 minutes

2 discussion promptsTotal 40 minutes

Your perception about Recommender Systems20 minutes
Non-personalized algorithms20 minutes

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 assignment1 peer review2 discussion prompts

12 videosTotal 35 minutes

Welcome by the instructor - module overview1 minute
Quality of Recommender Systems1 minute
Quality Indicators3 minutes
Online Evaluation Techniques3 minutes
Offline Evaluation Techniques3 minutes
Dataset Partitioning4 minutes
Overfitting2 minutes
Recap by the instructor1 minute
Error Metrics3 minutes
Classification Metrics4 minutes
Ranking Metrics7 minutes
Conclusions by the instructor1 minute

1 assignmentTotal 40 minutes

Module 2 - Graded Assessment40 minutes

1 peer reviewTotal 45 minutes

Missing ratings as negative ratings45 minutes

2 discussion promptsTotal 45 minutes

Ethical concerns and the impact of recommendations in our decision-making30 minutes
How an ideal recommendation should be?15 minutes

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 assignment1 peer review2 discussion prompts

9 videosTotal 19 minutes

Welcome by the instructor - module overview1 minute
Content-based Filtering2 minutes
Cosine Similarity5 minutes
Matrix Notation1 minute
K-Nearest Neighbours3 minutes
Recap by the instructor1 minute
Improving the ICM2 minutes
TF-IDF3 minutes
Conclusions by the instructor1 minute

1 assignmentTotal 45 minutes

Module 3 - Graded Assessment45 minutes

1 peer reviewTotal 60 minutes

Content-Based recommenders using item similarity60 minutes

2 discussion promptsTotal 70 minutes

Discuss advantages and limit of a content-based approach40 minutes
Get help with Content-Based Filtering30 minutes

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.