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Since recommender systems train and learn over the data they recommended themselves, they will never train over, learn, or recommend items that they didn’t already recommend for some reason, such as insufficient ranking to be seen by the user. It’s important to break this Feedback Loop in order to ensure that suitable recommendations aren’t missed. But you must strike a balance between deviating (just enough) from the system’s predictions through exploration and not defeating the system’s purpose altogether. In this liveProject, you’ll learn three methods of exploration that help you provide better recommendations to your users, as well as the costs and benefits of each.
This liveProject is for data scientists with theoretical knowledge of machine learning, deep learning, and recommender systems who want to take the next step in their career. To begin these liveProjects you will need to be familiar with the following:
In this liveProject, you’ll learn to use exploration to improve your system’s recommendations:
geekle is based on a wordle clone.