EIT Digital

Advanced Recommender Systems

Taught in English

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3,185 already enrolled

Course

Gain insight into a topic and learn the fundamentals

Paolo Cremonesi

Instructor: Paolo Cremonesi

3.7

(22 reviews)

Intermediate level

Recommended experience

14 hours to complete
3 weeks at 4 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • You will be able to use some machine learning techniques in order to build more sophisticated recommender systems.

  • You will learn how to combine different basic approaches into a hybrid recommender system, in order to improve the quality of recommendations.

  • You will know how to integrate different kinds of side information (about content or context) in a recommender system.

  • You'll learn how to use factorization machines and represent the input data, mixing together different kinds of filtering techniques.

Details to know

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Assessments

4 quizzes

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There are 5 modules in this course

In this first module, we will see how to apply machine learning to collaborative filtering techniques. We will learn how to write an item-based collaborative algorithm which is able to automatically learn the best similarities between items, in order to provide improved recommendations that better match the user opinions predicted by the model with the true user opinions. We will also understand how to train collaborative filtering algorithms that minimize this gap. We will finally define a new error metric based on ranking comparisons, useful to design learning-to-rank algorithms.

What's included

7 videos2 readings1 quiz2 peer reviews

In this second module, we will study a new family of collaborative filtering techniques based on dimensionality reduction and matrix factorization approaches, all inspired by SVD (Singular Value Decomposition). We will see the difference between memory-based and model-based recommender systems, discussing their limitations and advantages. In particular, we will learn how to turn basic matrix factorization algorithms from memory-based into model-based approaches. We will also analyse a new important parameter, the number of latent features. We will learn how to choose the correct number of latent features in order to provide personalised recommendations and to reduce the risk of overfitting historical data.

What's included

8 videos1 quiz1 peer review1 discussion prompt

In this third module, we will see how to combine two or more basic algorithms, such as collaborative filtering and content-based techniques, into a hybrid recommender system, in order improve the quality recommendations. We will study different hybridization approaches, from the simplest heuristic-based, to the more sophisticated machine learning-based. Thanks to hybrid techniques, we will be able to enrich the input of a collaborative recommender system with either content or contextual information.

What's included

10 videos1 quiz1 peer review2 discussion prompts

In this fourth and last module, we will introduce a new advanced technique of collaborative filtering with side information, which is called Factorization Machine (FM), and we’ll see how the input data should be represented when using this technique. With only one mathematical model, based on how you build the input table, we will be able to create a simple matrix factorization algorithm or a sophisticated collaborative filtering algorithm with side information (context, attributes on items or attributes on users). We will also discuss benefits and critical issues of algorithms based on FMs. At the end of the module you will know how to use FMs to mix together different kinds of filtering techniques and how to balance different kinds of input information, playing with coefficients and weights, in order to make better and more sophisticated predictions.

What's included

7 videos1 quiz1 peer review1 discussion prompt

The RecSys Challenge is the best way to train your competences: it's a practical exercise which provides a "hands-on" opportunity to put to good use and improve what you've been learning during this course (learning by doing). The application domain is an online store, the dataset we provide contains 4 months of transactions collected from an online supermarket. The main goal of the competition is to discover which item a user will interact with. The RecSys Challenge is optional and it is not required to pass the course. If you complete it, you will receive an Honors designation on your Course certificate.

What's included

1 reading1 programming assignment

Instructor

Instructor ratings
4.2 (5 ratings)
Paolo Cremonesi
EIT Digital
2 Courses5,123 learners

Offered by

EIT Digital

Recommended if you're interested in Machine Learning

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3.7

22 reviews

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SW
5

Reviewed on Jun 24, 2021

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