In the last years a lot of improvements were done in the field of Machine Learning and the Tools that support the community of developers. But still, implementing a recommender system is very hard. That is why at Crossing Minds, we decided to create a series of 4 meetups to discuss how to implement a recommender system end-to-end:

Part 1 – The Right Dataset
Part 2 – Model Training
Part 3 – Model Evaluation
Part 4 – Real-Time Deployment

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The third meetup will be about evaluating different models for our recommender system. We will review the strategies we have to check if a model is under fitting or overfitting. After that, we will present and analyze the losses that are typically used in recommendation systems to train models. We will compare regression, classification, and rank based losses and when it's convenient to use each one. Finally, we are going to cover all the metrics that are typically used to evaluate the performance of different recommendation systems and how to test that the models are giving good results in production.