Building Recommender Systems with Machine Learning and AI: Help people discover new products and content with deep learning, neural networks, and machine learning recommendations.: Kane, Frank: 9798769079467: Amazon.com: Books

Building a recommendation engine Evaluating recommender systems Content-based filtering using item attributes Neighborhood-based collaborative filtering with user-based, item-based, and KNN CF Model-based methods including matrix factorization and SVD Applying deep learning, AI, and artificial neural networks to recommendations Session-based recommendations with recursive neural networks Scaling to massive data sets with Apache Spark machine learning, Amazon DSSTNE deep learning, and AWS SageMaker with factorization machines Using the Tensorflow Recommenders Framework (TFRS) to develop and deploy deep learning-based recommender systems Using SaaS platforms such as Amazon Personalize, Recombee, and RichRelevance Using Generative Adversarial Networks (GAN's) to generate user recommendations Real-world challenges and solutions with recommender systems Case studies from YouTube and Netflix Building hybrid, ensemble recommenders Using Generative Adversarial Networks (GAN's) to generate user recommendations