Recommender Systems: An Applied Approach using Deep Learning

Overview

Recommender systems are used in various areas with commonly recognized examples, including playlist generators for video and music services, product recommenders for online stores and social media platforms, and open web content recommenders. Recommender systems have also been developed to explore research articles and experts, collaborators, and financial services. The course begins with an introduction to deep learning concepts to develop recommender systems and a course overview. The course advances to topics covered, including deep learning for recommender systems, understanding the pros and cons of deep learning, recommendation inference, and deep learning-based recommendation approach. You will then explore neural collaborative filtering and learn how to build a project based on the Amazon Product Recommendation System. You will learn to install the required packages, analyze data for product recommendations, prepare data, and model development using a two-tower approach. You will learn to implement a TensorFlow recommender and test a recommender model. You will make predictions using the built recommender system. Upon completion, you can relate the concepts and theories for recommender systems in various domains and implement deep learning models for building real-world recommendation systems. This course is designed for individuals looking to advance their skills in applied deep learning, understand relationships of data analysis with deep learning, build customized recommender systems for their applications, and implement deep learning algorithms for recommender systems. The prerequisites include a basic to intermediate knowledge of Python and Pandas library.

Syllabus

Introduction

In this module, we will introduce you to the instructor, providing a brief overview of their background and teaching style. You will also get a comprehensive outline of the course, including the main topics and concepts that will be covered, setting the stage for your learning journey ahead.

Deep Learning Foundation for Recommender Systems

In this module, we will delve into the foundational aspects of deep learning as it pertains to recommender systems. You will gain insights into transitioning from machine learning to deep learning, deploying models for inference, and understanding the intricacies of neural and variational autoencoder collaborative filtering. Additionally, you will explore the pros and cons of deep learning models and assess their effectiveness in recommender systems.

Project Amazon Product Recommendation System

In this module, we will guide you through creating a project that develops an Amazon product recommendation system. You will learn to use TensorFlow Recommenders, implement the two-tower model, and visualize data with WordCloud. The lessons cover downloading necessary libraries, preparing and rating data, performing train-test splits, and building the model. Finally, we will evaluate model accuracy and generate product recommendations.