Basic Recommender Systems

Basic Recommender Systems

Instructor: Paolo Cremonesi

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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

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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.

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 videos Total 29 minutes

Course overview and welcome by the instructor 3 minutes
Welcome by the instructor - module overview 2 minutes
Introduction to Recommender Systems 4 minutes
Taxonomy of Recommender Systems 6 minutes
Item-Content Matrix 2 minutes
User-Rating Matrix 3 minutes
Inferring Preferences 3 minutes
Recap by the instructor 1 minute
Non-personalized Recommender Systems 3 minutes
Global Effects 2 minutes
Conclusions by the instructor 1 minute

2 readings Total 15 minutes

Course Syllabus 10 minutes
Credits & Acknowledgements 5 minutes

1 assignment Total 45 minutes

Module 1 - Graded Assessment 45 minutes

1 peer review Total 45 minutes

Differences between implicit and explicit ratings 45 minutes

2 discussion prompts Total 40 minutes

Your perception about Recommender Systems 20 minutes
Non-personalized algorithms 20 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 videos Total 35 minutes

Welcome by the instructor - module overview 1 minute
Quality of Recommender Systems 1 minute
Quality Indicators 3 minutes
Online Evaluation Techniques 3 minutes
Offline Evaluation Techniques 3 minutes
Dataset Partitioning 4 minutes
Overfitting 2 minutes
Recap by the instructor 1 minute
Error Metrics 3 minutes
Classification Metrics 4 minutes
Ranking Metrics 7 minutes
Conclusions by the instructor 1 minute

1 assignment Total 40 minutes

Module 2 - Graded Assessment 40 minutes

1 peer review Total 45 minutes

Missing ratings as negative ratings 45 minutes

2 discussion prompts Total 45 minutes

Ethical concerns and the impact of recommendations in our decision-making 30 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 videos Total 19 minutes

Welcome by the instructor - module overview 1 minute
Content-based Filtering 2 minutes
Cosine Similarity 5 minutes
Matrix Notation 1 minute
K-Nearest Neighbours 3 minutes
Recap by the instructor 1 minute
Improving the ICM 2 minutes
TF-IDF 3 minutes
Conclusions by the instructor 1 minute

1 assignment Total 45 minutes

Module 3 - Graded Assessment 45 minutes

1 peer review Total 60 minutes

Content-Based recommenders using item similarity 60 minutes

2 discussion prompts Total 70 minutes

Discuss advantages and limit of a content-based approach 40 minutes
Get help with Content-Based Filtering 30 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.