Monthly Subscription Starting at AED 99 + VAT

Advanced Recommender Systems
  • Level Professional
  • المدة 15 ساعات hours
  • الطبع بواسطة EIT Digital
  • Offered by

عن

In this course, you will see how to use advanced machine learning techniques to build more sophisticated recommender systems. Machine Learning is able to provide recommendations and make better predictions, by taking advantage of historical opinions from users and building up the model automatically, without the need for you to think about all the details of the model. At the end of this course, you will learn how to manage hybrid information and how to combine different filtering techniques, taking the best from each approach. You will know how to use factorization machines and represent the input data accordingly. You will be able to design more sophisticated recommender systems, which can solve the cross-domain recommendation problem. You will also learn how to identify new trends and challenges in providing recommendations in a range of innovative application contexts. This course leverages two important EIT Digital Overarching Learning Outcomes (OLOs), related to your 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 outcomes. 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 solve real-life problems in complex and innovative scenarios.

الوحدات

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 & Aknowledgements

1.1 WELCOME BY THE INSTRUCTOR

    • 1 Videos
Show info about module content

1 Videos

  • Welcome by the instructor - module overview

1.2 ITEM-BASED CF AS OPTIMIZATION PROBLEM

    • 1 Videos
Show info about module content

1 Videos

  • Item-Based CF as Optimization Problem

1.3 SLIM

    • 1 Videos
Show info about module content

1 Videos

  • SLIM

1.4 RECAP BY THE INSTRUCTOR

    • 1 Videos
    • 1 PeerReview
Show info about module content

1 Peer Review

  • SLIM

1 Videos

  • Recap by the instructor

1.5 BAYESIAN PROBABILISTIC RANKING (BPR)

    • 1 Videos
    • 1 PeerReview
Show info about module content

1 Peer Review

  • BPR

1 Videos

  • Bayesian Probabilistic Ranking

1.6 CONCLUSION BY THE INSTRUCTOR

    • 1 Videos
    • 1 Assignment
Show info about module content

1 Assignment

  • Module 1 Advanced - Graded Assessment

1 Videos

  • Conclusions by the instructor

2.1 WELCOME BY THE INSTRUCTOR

    • 1 Videos
Show info about module content

1 Videos

  • Welcome by the instructor

2.2 MATRIX FACTORIZATION

    • 1 Videos
Show info about module content

1 Videos

  • Matrix Factorization

2.3 FUNK SVD

    • 1 Videos
Show info about module content

1 Videos

  • Funk SVD

2.4 SVD++

    • 1 Videos
Show info about module content

1 Videos

  • SVD++

2.5 RECAP BY THE INSTRUCTOR

    • 1 Videos
Show info about module content

1 Videos

  • Recap by the instructor

2.6 ASYMMETRIC SVD

    • 1 Videos
Show info about module content

1 Videos

  • Asymmetric SVD

2.7 PURE SVD

    • 1 Videos
    • 1 PeerReview
Show info about module content

1 Peer Review

  • Recommending items

1 Videos

  • Pure SVD

2.8 CONCLUSIONS BY THE INSTRUCTOR

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

1 Assignment

  • Module 2 Advanced - Graded Assessment

1 Discussions

  • Explainability in Machine Learning

1 Videos

  • Conclusions by the instructor

3.1 WELCOME BY THE INSTRUCTOR

    • 1 Videos
Show info about module content

1 Videos

  • Welcome by the instructor

3.2 HYBRID RECOMMENDER SYSTEMS

    • 1 Videos
Show info about module content

1 Videos

  • Hybrid Recommender Systems

3.3 LINEAR COMBINATION

    • 1 Videos
Show info about module content

1 Videos

  • Linear Combination

3.4 LIST COMBINATION

    • 1 Videos
Show info about module content

1 Videos

  • List Combination

3.5 PIPELINING

    • 1 Videos
Show info about module content

1 Videos

  • Pipelining

3.6 RECAP BY THE INSTRUCTOR

    • 1 Videos
Show info about module content

1 Videos

  • Recap by the instructor

3.7 MERGING MODELS

    • 1 Videos
Show info about module content

1 Videos

  • Merging Models

3.8 COLLABORATIVE FILTERING WITH SIDE INFORMATION

    • 1 Videos
Show info about module content

1 Videos

  • CF with Side Information

3.9 CONTEXT-AWARE RECOMMENDER SYSTEMS

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

1 Peer Review

  • Tensor-based factorization

1 Discussions

  • Preferences in context

1 Videos

  • Context-Aware Recommender Systems

3.10 CONCLUSIONS BY THE INSTRUCTOR

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

1 Assignment

  • Module 3 Advanced - Graded Assessment

1 Discussions

  • A matter of weights

1 Videos

  • Conclusions by the instructor

4.1 WELCOME BY THE INSTRUCTOR

    • 1 Videos
Show info about module content

1 Videos

  • Welcome by the instructor

4.2 FACTORIZATION MACHINES

    • 1 Videos
Show info about module content

1 Videos

  • Factorization Machines

4.3 RECAP BY THE INSTRUCTOR

    • 1 Videos
    • 1 PeerReview
Show info about module content

1 Peer Review

  • Factorization Machines

1 Videos

  • Recap by the instructor

4.4 EXPLAINING FM's MODEL

    • 1 Videos
Show info about module content

1 Videos

  • Explaining FM's Model

4.5 EXTENDING THE MODEL

    • 1 Videos
Show info about module content

1 Videos

  • Extending the model

4.6 SOLVING THE IMBALANCE PROBLEM

    • 1 Videos
Show info about module content

1 Videos

  • Solving the imbalance problem

4.7 CONCLUSIONS BY THE INSTRUCTOR

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

1 Assignment

  • Module 4 Advanced - Graded Assessment

1 Discussions

  • Multimedia contents

1 Videos

  • Conclusions by the instructor

Recsys Challenge on Kaggle

    • 1 Readings
    • 1 Programming
Show info about module content

1 Programming

  • RecSys Challenge on Kaggle

1 Readings

  • The RecSys Challenge

Auto Summary

Unlock the power of advanced machine learning with the "Advanced Recommender Systems" course in Data Science & AI. Guided by expert instructors from Coursera, this professional-level course spans 900 minutes, offering in-depth knowledge on building sophisticated recommender systems. Learn to manage hybrid information, combine filtering techniques, and solve cross-domain recommendation challenges. Ideal for professionals aiming to innovate and improve recommendation tools in complex scenarios. Available via professional subscription.

Paolo Cremonesi
Instructor

Paolo Cremonesi