The School of Computer Science Presents...
Technical Series Workshop
Recommendation Systems in Python (Part1)
Presenter: Shaghayegh Sadeghi
Date: Wednesday, January 24th, 2024
Time: 2:45 pm – 3:45 pm
Location: 4th Floor (Workshop space) at 300 Ouellette Avenue (School of Computer Science Advanced Computing Hub)
LATECOMERS WILL NOT BE ADMITTED once the presentation has begun.
Abstract: 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.
Workshop Outline:
Introduction to Recommendation Engines
What are recommendation engines?
Recommendation engines vs. predictions
Identifying the correct data for recommendation engines
Implicit vs. explicit data
Non-personalized recommendations
Introduction to non-personalized recommendations
Improved non-personalized recommendations
Combining popularity and reviews
Personalized suggestions
Finding all pairs of movies
Counting up the pairs
Making your first movie recommendations
Content-Based Recommendations
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
Prerequisites: Knowledge of Supervised Learning with scikit-learn and pandas.
Biography: Shaghayegh is a Ph.D. student at the School of Computer Science (University of Windsor). Her main research interest is in biological network embedding.
