Reducing the Friction for Building Recommender Systems with Merlin

Recommender Systems (RecSys) are the engine of the modern internet and the catalyst for human decisions. The goal of a recommender system is to generate relevant recommendations for users from a collection of items or services that might interest them. Building a recommendation system is challenging because it requires multiple stages (item retrieval, filtering, ranking, ordering) to work together seamlessly and efficiently during training and inference. The biggest challenges faced by new practitioners are the lack of understanding around what RecSys look like in the real world and the difficulty in transitioning from the simple Matrix Factorization (MF) to more complex deep learning architectures with multiple input features, neural components and prediction heads.