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Recommender Systems

Course Overview

I teach CS608 Recommender Systems which is offered as an elective to students enrolled in Master of IT in Business (MITB) programme at School of Information Systems (SIS), Singapore Management University (SMU). The course objective is to provide students with a conceptual understanding of the fundamental algorithms powering recommender systems as well as with practical know-how on designing, training, and deploying a recommender system in various applications. In addition to regular lectures, there are significant hands-on components involving coding exercises in Python, as well as individual and group projects.

For an overview of the course, check out the following introduction slides that we used in the first week.

Roadmap for CS608 Recommender Systems ‎‎‎‎‎‎‎‎‎‎(circa 2020)‎‎‎‎‎‎‎‎‎‎


Topics

  • Neighborhood-Based Collaborative Filtering
  • Matrix Factorization
  • Learning Algorithms
  • Evaluation Measures
  • Implicit Feedback
  • Multimodality
  • Contextual Awareness
  • Explanations
  • Retrieval
  • Deep Learning
The course is accompanied by a set of tutorials (aligned with the topics above) based on the Cornac recommender systems library.


References

The materials are in part based on the following sources:
You may also find the following references useful:

Teaching Team

AY2019/2020 Term 3

(Hady Lauw, Jean Chen Yun-Chen, Tuan Truong)