Today, recommender systems are widely used in various industries such as video streaming services, online shopping, and content creation and sharing. Thanks to the development of deep learning and artificial intelligence (AI), recommender systems can use the large amount of data that is generated by users’ everyday selections and make high-quality recommendations.
This tutorial introduces a method that is now widely used and has shown to perform well in industry – deep learning-based collaborative filtering. Deep learning-based recommenders have proven to have much better performance than classic machine learning methods because deep learning models can use the power of big data. Because collaborative filtering makes recommendations based on similar users, the more user feedback and purchase data you have, the more accurate recommendations the machine learning model can make.
If your company has user purchase history and ratings data, and you hope to use that data to recommend new products to users