Recommender Systems

Recommender Systems (RecSys) are ubiquitous and have become inseparable parts of our everyday lives. They are central to our experience with the digital world in everything we do and every service we consume. They help us find our favourite items to purchase, our friends on social networks, our favourite movies to watch, music to listen to, and books to read. Nowadays, RecSys have numerous applications that transcend these classic taste-driven domains of social media and entertainment and are revolutionising several sectors. Education, industriy, healthcare, tourism, transport and logistics are among the areas where the applications of RecSys have gained momentum.
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Recommender Systems: an overview

This course is designed to get you introduced to Recommender systems and provide you with the state-of-the-art tools and algorithms to design and build RecSys engines. By the end of this course you will be able to:

  • Develop foundational knowledge on Recommendation systems.
  • Understand a wide variety of Recommendation system algorithms.
  • Understand how to design and evaluate Recommendation systems in different application domains.
  • Apply the learned skills to design Recommendation engines using real datasets, evaluate the designed engines and report results.
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Program

Decemeber 05 - 09, 2022, Belval campus (MSA), University of Luxembourg
- Limited number of participants.
Enrolment via Moodle opens on 28/10/2022


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Day 1

Introduction to Recommender Systems

  • Personalisation: The Big Picture
  • Overview of SOTA RecSys Methods (ML, DL, RL) based approaches.

    • Day 2

    Content-Based (CB) & Collaborative Filtering (CF)

  • Lecture
  • Invited talk
  • Hands-on Case-Studies (CB & CF)

    • Day 3

    Hybrid Approaches & Reinforcement Learning (RL) based RecSys

  • Lecture
  • Invited talk
  • Hands-on Case-Studies (Hybrid & RL)

    • Day 4

    Modern RecSys & Open Chalenges

  • Lecture
  • Course project topics
  • Hands-on (Course Project)

    • Day 5

    Project presentation & Closing

  • Invited talk
  • Student Presentations

    • Meet Our Speakers

    Course Instructor

    Dr. Bereket YILMA

    University of Luxembourg, Luxembourg


    Course Instructor

    Invited Talks

    Invited talk
    Improving Personalized Explanation Generation through Visualization

    Dr. Lei Li

    Hong Kong Baptist University (HKBU)

    Invited talk
    Explainability in Recommender Systems

    Dr. Ludovik Coba

    Expedia Group, United Kingdom

    Invited talk
    Language models in Recommender Systems

    Yuhui Zhang

    Amazon AWS AI Labs & Stanford University, USA

    Invited talk
    DALL-E-2, realistic images and art generation from text prompt

    Victor Silva

    OpenAI & University of Alberta, Canada

    • ©2022 Bereket A. YILMA.