June 6, 2019

Recommender Systems from A to Z

The Right Dataset

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

This first meetup will be about building the right dataset and doing all the preprocessing needed to create different models. We will talk about explicit vs implicit feedback, dataset analysis, likes/dislikes vs ratings, users and items features, normalization and similarities.