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.
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Basic Recommender Systems
EIT DigitalAbout this Course
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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
Offered by
EIT Digital
EIT Digital is a pan-European organization whose mission is 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.
Syllabus - What you will learn from this course
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
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
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)
12 videos
Quality of Recommender Systems1m
Quality Indicators3m
Online Evaluation Techniques3m
Offline Evaluation Techniques2m
Dataset Partitioning4m
Overfitting1m
Recap by the instructor1m
Error Metrics3m
Classification Metrics4m
Ranking Metrics7m
Conclusions by the instructor1m
1 practice exercise
Module 2 - Graded Assessment40m
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)
9 videos
Content-based Filtering2m
Cosine Similarity4m
Matrix Notation1m
K-Nearest Neighbours2m
Recap by the instructor1m
Improving the ICM2m
TF-IDF2m
Conclusions by the instructor54s
1 practice exercise
Module 3 - Graded Assessment45m
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)
9 videos
Collaborative Filtering2m
User-based CF13m
Recap by the instructor1m
Item-based CF12m
User-based vs. Item-based2m
Model-based vs. Memory-based5m
Recommendation as Association Rules2m
Conclusions by the instructor48s
1 practice exercise
Module 4 - Graded Assessment30m
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