Technical Workshop Series
Natural Language Processing (NLP) for Recommender Systems (1st Offering)
Presenter: Soroush Ziaeinejad
Date: Monday, June 24th, 2024
Time: 1:00 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:
Discover the synergy between NLP and Recommender Systems in this hands-on workshop. Learn how to leverage NLP techniques to extract insights from textual data and create personalized recommendations. Dive into text preprocessing, feature extraction (such as TF-IDF and word embeddings) and building content-based recommender systems. Join us in unlocking the power of NLP to revolutionize recommendation engines and enhance user experiences. Participants will also get hands-on experience with NLP tools and libraries, such as NLTK and Gensim in Python.
Workshop Outline:
- Introduction to NLP in Recommender Systems
- Text Preprocessing Techniques
- Feature Extraction from Text Data
- Building Content-Based Recommender Systems
- Hands-on Coding Session: Implementing NLP in Recommender Systems
- Evaluation Metrics for Recommender Systems
In this workshop, participants will explore how NLP techniques can enhance personalized recommendations. Through hands-on exercises and practical examples in Python, attendees will learn text preprocessing, feature extraction, and building content-based recommender systems. The workshop will also cover challenges and considerations in implementing NLP-powered recommendation engines. By the end, participants will have the skills to apply NLP in creating smarter and more tailored recommendation systems.
Prerequisites:
Basic knowledge of NLP, Python, and mathematics
Biography:
Soroush is a Ph.D. student and research assistant at the School of Computer Science. His main research area is Natural Language Processing and Information Retrieval on social networks.