Recommender Systems

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Contents

1 Course Overview
2 Topics
3 References
4 Teaching Team

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:

Recommender Systems: The Textbook by Charu C. Aggarwal
Cornac: A Comparative Framework for Multimodal Recommender Systems by Aghiles Salah, Quoc-Tuan Truong, and Hady W. Lauw

You may also find the following references useful:

Recommender Systems: An Introduction by Dietmar Janach, Markus Zanker, Alexander Felfernig, Gerhard Friedrich
Statistical Methods for Recommender Systems by Deepak K. Agarwal and Bee-Chung Chen

Teaching Team

AY2019/2020 Term 3

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