The School of Computer Science Technical Workshop Series Presents: Recommendation Systems in Python (Part 2) by: Shaghayegh Sadeghi

Thursday, November 3, 2022 - 12:00 to 13:00

The School of Computer Science at the University of Windsor is pleased to present...

Technical Workshop Presentation by: Shaghayegh Sadeghi – PhD Candidate

Recommendation Systems in Python (Part 2)

Date: Thursday, November 3rd, 2022 

Time: 12:00 pm – 1:00 pm 

Location: 4th Floor (Workshop space) at 300 Ouellette Avenue (School of Computer Science Advanced Computing Hub)


In this workshop, students will learn everything they need to know to create their own recommendation engine. Through hands-on exercises, students will get to grips with the two most common systems, collaborative filtering and content-based filtering. Next, students will learn how to measure similarities like the Jaccard distance and cosine similarity and how to evaluate the quality of recommendations on test data using the root mean square error (RMSE). By the end of this course, students will have built their very own movie recommendation engine and be able to apply their Python skills to create these systems for any industry.


Knowledge of Supervised Learning with scikit-learn and pandas

Workshop Outline:

• Intro to content-based recommendations

• Why use content-based models?

• Creating content-based data

• Understanding the content-based data

Making content-based recommendations

• Comparing individual movies with Jaccard similarity

• Comparing all your movies at once

• Making recommendations based on movie genres

Text-based similarities

• Instantiate the TF-IDF model

• Creating the TF-IDF Data Frame

• Comparing all your movies with TF-IDF

• Making recommendations with TF-IDF

User profile recommendations

• Build the user profiles

• User profile-based recommendations


Shaghayegh is a PhD candidate and research assistant in the School of Computer Science. Her main research interest is in using Graph Neural Networks for graph embedding.

Research interest: Privacy and security of machine learning, Biometrics, and Digital Forensics.