Recommender Systems

4 hours of instruction

This course provides an overview of how recommender systems work and teaches students how to build effective models. By the end of this course, students will be able to explain the key assumptions underlying recommender systems and build and evaluate them based on real data.

OBJECTIVES

1. Identify and define use cases for recommender systems
2. Build and evaluate content-based recommender systems
3. Build and evaluate item-based filtering algorithm

PREREQUISITES

Data Wrangling in Python

SYLLABUS & TOPICS COVERED

1. Basics
   • Recommender systems use cases and logic behind them
   • Data processing for recommender system
2. Content Based Recommenders
   • Content-based recommender system use cases and logic behind it
   • Generate recommendations using content-based recommender system
3. Collaborative Filtering
   • Collaborative filtering use cases and logic behind it
   • SVD and its role in recommender systems
   • Generate recommendations using collaborative filtering recommender system

SOFTWARE REQUIREMENTS

You will have access to a Python-based JupyterHub environment for this course. No additional download or installation is required.