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Basic Recommender Systems
  • Level Professional
  • Duration 12 hours
  • Course by EIT Digital
  • Offered by

About

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.

Modules

COURSE OVERVIEW

    • 1 Videos
    • 2 Readings
Show info about module content

1 Videos

  • Course overview and welcome by the instructor

2 Readings

  • Course Syllabus
  • Credits & Acknowledgements

1.1 WELCOME AND MODULE OVERVIEW

    • 1 Videos
    • 1 Discussion
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1 Discussions

  • Your perception about Recommender Systems

1 Videos

  • Welcome by the instructor - module overview

1.2 INTRODUCTION TO RECOMMENDER SYSTEMS

    • 1 Videos
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1 Videos

  • Introduction to Recommender Systems

1.3 TAXONOMY OF RECOMMENDER SYSTEMS

    • 1 Videos
    • 1 Discussion
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1 Discussions

  • Non-personalized algorithms

1 Videos

  • Taxonomy of Recommender Systems

1.4 ITEM-CONTENT MATRIX

    • 1 Videos
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1 Videos

  • Item-Content Matrix

1.5 USER-RATING MATRIX

    • 1 Videos
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1 Videos

  • User-Rating Matrix

1.6 INFERRING PREFERENCES

    • 1 Videos
    • 1 PeerReview
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1 Peer Review

  • Differences between implicit and explicit ratings

1 Videos

  • Inferring Preferences

1.7 RECAP BY THE INSTRUCTOR

    • 1 Videos
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1 Videos

  • Recap by the instructor

1.8 NON-PERSONALIZED RECOMMENDERS

    • 1 Videos
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1 Videos

  • Non-personalized Recommender Systems

1.9 GLOBAL EFFECTS

    • 1 Videos
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1 Videos

  • Global Effects

1.10 CONCLUSIONS BY THE INSTRUCTOR

    • 1 Videos
    • 1 Assignment
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1 Assignment

  • Module 1 - Graded Assessment

1 Videos

  • Conclusions by the instructor

2.1 WELCOME AND MODULE OVERVIEW

    • 1 Videos
Show info about module content

1 Videos

  • Welcome by the instructor - module overview

2.2 QUALITY OF RECOMMENDER SYSTEMS

    • 1 Videos
    • 1 Discussion
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1 Discussions

  • Ethical concerns and the impact of recommendations in our decision-making

1 Videos

  • Quality of Recommender Systems

2.3 QUALITY INDICATORS FOR RECOMMENDER SYSTEMS

    • 1 Videos
    • 1 Discussion
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1 Discussions

  • How an ideal recommendation should be?

1 Videos

  • Quality Indicators

2.4 ONLINE EVALUATION TECHNIQUES

    • 1 Videos
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1 Videos

  • Online Evaluation Techniques

2.5 OFFLINE EVALUATION TECHNIQUES

    • 1 Videos
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1 Videos

  • Offline Evaluation Techniques

2.6 DATASET PARTITIONING

    • 1 Videos
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1 Videos

  • Dataset Partitioning

2.7 OVERFITTING

    • 1 Videos
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1 Videos

  • Overfitting

2.8 RECAP BY THE INSTRUCTOR

    • 1 Videos
    • 1 PeerReview
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1 Peer Review

  • Missing ratings as negative ratings

1 Videos

  • Recap by the instructor

2.9 ERROR METRICS

    • 1 Videos
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1 Videos

  • Error Metrics

2.10 CLASSIFICATION METRICS

    • 1 Videos
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1 Videos

  • Classification Metrics

2.11 RANKING METRICS

    • 1 Videos
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1 Videos

  • Ranking Metrics

2.12 CONCLUSIONS BY THE INSTRUCTOR

    • 1 Videos
    • 1 Assignment
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1 Assignment

  • Module 2 - Graded Assessment

1 Videos

  • Conclusions by the instructor

3.1 WELCOME AND MODULE OVERVIEW

    • 1 Videos
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1 Videos

  • Welcome by the instructor - module overview

3.2 CONTENT BASED FILTERING

    • 1 Videos
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1 Videos

  • Content-based Filtering

3.3 COSINE SIMILARITY

    • 1 Videos
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1 Videos

  • Cosine Similarity

3.4 MATRIX NOTATION

    • 1 Videos
    • 1 Discussion
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1 Discussions

  • Discuss advantages and limit of a content-based approach

1 Videos

  • Matrix Notation

3.5 K-NEAREST NEIGHBOURS

    • 1 Videos
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1 Videos

  • K-Nearest Neighbours

3.6 RECAP BY THE INSTRUCTOR

    • 1 Videos
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1 Videos

  • Recap by the instructor

3.7 IMPROVING THE ITEM-CONTENT MATRIX

    • 1 Videos
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1 Videos

  • Improving the ICM

3.8 TF - IDF: TERM FREQUENCY - INVERSE DOCUMENT FREQUENCY

    • 1 Videos
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1 Videos

  • TF-IDF

3.9 CONCLUSIONS BY THE INSTRUCTOR

    • 1 Videos
    • 1 Discussion
    • 1 PeerReview
    • 1 Assignment
Show info about module content

1 Assignment

  • Module 3 - Graded Assessment

1 Peer Review

  • Content-Based recommenders using item similarity

1 Discussions

  • Get help with Content-Based Filtering

1 Videos

  • Conclusions by the instructor

4.1 WELCOME AND MODULE OVERVIEW

    • 1 Videos
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1 Videos

  • Welcome by the instructor - module overview

4.2 COLLABORATIVE FILTERING

    • 1 Videos
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1 Videos

  • Collaborative Filtering

4.3 COLLABORATIVE FILTERING: USER-BASED

    • 1 Videos
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1 Videos

  • User-based CF

4.4 RECAP BY THE INSTRUCTOR

    • 1 Videos
Show info about module content

1 Videos

  • Recap by the instructor

4.5 COLLABORATIVE FILTERING: ITEM-BASED

    • 1 Videos
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1 Videos

  • Item-based CF

4.6 USER-BASED vs. ITEM-BASED

    • 1 Videos
    • 1 Discussion
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1 Discussions

  • User-based and Item-based approaches

1 Videos

  • User-based vs. Item-based

4.7 MODEL-BASED vs. MEMORY-BASED

    • 1 Videos
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1 Videos

  • Model-based vs. Memory-based

4.8 RECOMMENDATION AS ASSOCIATION RULES

    • 1 Videos
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1 Videos

  • Recommendation as Association Rules

4.9 CONCLUSIONS BY THE INSTRUCTOR

    • 1 Videos
    • 1 Discussion
    • 1 PeerReview
    • 1 Assignment
Show info about module content

1 Assignment

  • Module 4 - Graded Assessment

1 Peer Review

  • Item-Based CF and similarity functions

1 Discussions

  • Get help with Collaborative Filtering

1 Videos

  • Conclusions by the instructor

Auto Summary

Discover the intricacies of recommender systems with this professional course focused on big data and analytics. Led by expert instructors, it covers collaborative and content-based techniques, essential algorithms, and evaluation methods. Gain the skills to design, measure, and improve recommender systems for various application domains, considering social and ethical issues. This 720-minute course, available through Coursera's Starter subscription, is perfect for professionals aiming to innovate and enhance recommendation tools for better decision-making and quality of life.

Paolo Cremonesi
Instructor

Paolo Cremonesi