Sharing a free event who are interested to learn about recommender systems
As practitioners, data scientists, researchers, and engineers, we tackle the challenges associated with building, training, optimizing, and deploying production-ready recommender systems. Providing relevant recommendations and personalized engagements is deeply challenging. Traditional methods, including matrix factorization, are useful. Yet with the availability of more nuanced contextual data, modern recommender systems are able to leverage techniques, methods, and algorithms beyond the user-item matrix formulation. Diving into recommender nuances and leveraging an ensemble of tools, methods, packages, and libraries can help us fine-tune and scale our efforts.
During this summit, hear learnings and best practices from fellow data scientists and engineers on how they built and deployed effective modern recommender systems.