Technical Workshop Series
Unlocking the Power of Recommender Systems: A Hands-On Journey with Python - 2nd in series (1st Offering)
Presenter: Bahareh Rahmatikargar
Date: Thursday, July 4th, 2024
Time: 3:00 PM
Location: 4th Floor (Lecture Space) at 300 Ouellette Avenue (School of Computer Science Advanced Computing Hub)
This is a hands-on workshop, please bring your laptop.
Abstract: This workshop marks the second workshop in a three-part series dedicated to the fascinating field of social network analysis and its application in recommender systems. Step right into the fascinating world of recommender systems! In our previous workshop, we explored various types of recommender systems, from traditional models to the latest advancements. We discussed their strengths and limitations, the data they use, and how to measure their performance.
Now, get ready for the next level of excitement! In this workshop, we'll dive into the practical aspects of making recommender systems work. Using the Python programming language, we will demonstrate hands-on methods to create your own custom recommender systems. It's like learning cool tricks to tailor recommendations perfectly to your needs!
Whether you're already familiar with the basics or just starting out, we've got you covered. We will break down complex concepts into simple, easy-to-understand terms, making the learning process both enjoyable and engaging. You'll be mastering recommendations in no time!
Join us for this exciting workshop where we'll explain everything clearly and guide you in building your own recommender systems like a pro. Let's have fun and explore the magic of recommendations together
Workshop Outline:
- Explore practical approaches for building recommender systems: Through interactive and fun hands-on exercises using Python, you'll learn to implement your own custom recommender systems, starting with basic types and progressing to more advanced ones.
- Apply recommender systems to real-world scenarios: With the knowledge and skills gained, you'll be able to apply recommender systems to various applications, such as product recommendations, movie suggestions, and more.
Prerequisites: Familiarity with recommender systems and Knowing the Python programming language is a prerequisite
Biography: Bahareh Rahmati is an enthusiastic Ph.D. student who has started her program at the School of Computer Science in January 2021. Her research is in the field of data science and AI, with focus on graph-based recommendation systems. She has currently published multiple papers in top-tier venues.
MAC STUDENTS ONLY - Register here
Note: This is a series of three workshops, students are encouraged to attend all, but it is not necessary in order to earn points. Registration in the rest of the series is NOT automatic, students will need to sign up using the link for MAC students.