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 helps us fine-tune and scale our efforts.
Join us on July 28 for an engaging online conversation with experts from Netflix, Twitter, Weights & Biases, Coveo, and more to hear learnings and best practices on how they built and deployed effective modern recommender systems. After the event, attendees will also have access to our RecSys Summit China sessions from Alibaba, Tencent, Meituan in both English and Mandarin.