School of Computer Science
Technical Workshop Series: Natural Language Processing (NLP) for Recommender Systems
Presenter: Soroush Ziaeinejad
Location: 4th Floor (Workshop space) at 300 Ouellette Avenue (School of Computer Science Advanced Computing Hub)
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 to unlock the power of NLP in revolutionizing recommendation engines and enhancing user experiences. Participants will also get hands-on experience with NLP tools and libraries, such as NLTK and Gensim in Python 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.
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
Basic knowledge of Python and mathematics
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.