TECHNICAL WORKSHOP SERIES: Natural Language Processing (NLP) for Recommender Systems by Soroush Ziaeinejad

Monday, November 20, 2023 - 12:00


School of Computer Science

Technical Workshop Series: Natural Language Processing (NLP) for Recommender Systems

Presenter: Soroush Ziaeinejad

Date:  Monday, November 20th 12:00pm – 1:00pm

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.



Workshop Outline:

  • Introduction

  • 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

  • Conclusion



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.