Recommender

Help people discover new products and content with deep learning, neural networks, and machine learning recommendations.

Master Recommender Systems. Learn to design, build, and evaluate recommender systems for commerce and content.

Step-By-Step Guide to Build Collaborative Filtering and Association Rule Based Recommender Using Fastai and Python

How to build autoencoders and recommender systems with neural networks. Machine learning hands on data science class

Implementing Data Science Driven Recommender Systems For Business Applications With R

Recommender Systems: A Multi-Disciplinary Approach presents a multi-disciplinary approach for the development of recommender systems. It explains different types of pertinent algorithms with their comparative analysis and their role for different applications. This book explains the big data behind recommender systems, the marketing benefits, how to make good decision support systems, the role of machine learning and artificial networks, and the statistical models with two case studies. It shows how to design attack resistant and trust-centric recommender systems for applications dealing with sensitive data.