This tutorial introduces the Merlin framework which aims to make the development and deployment of recommender systems easier, providing methods for evaluating existing approaches, developing new ideas and deploying them to production. There are many techniques, such as different model architectures (e.g. MF, DLRM, DCN, etc), negative sampling strategies, loss functions or prediction tasks (binary, multi-class, multi-task) that are commonly used in these pipelines. Merlin provides building blocks that allow RecSys practitioners to focus on the “what” question in designing their model pipeline instead of “how”. Supporting research into new ideas within the RecSys spaces is equally important and Merlin supports the addition of custom components and the extension of existing ones to address gaps. In this tutorial, participants will learn: How to easily implement common recommender system techniques for comparison Deploying recommender systems - using an open source framework Merlin and its libraries Prerequisite(s): Basic familiarity with Python Deep learning and deep learning frameworks.
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