Every recommender system, from Netflix to Spotify, faces a critical initial challenge: the cold start problem.
How do you make intelligent recommendations for new users or new items when you have no interaction data? In the upcoming Sahaj Software DevDay in London, Enrico will dive into models that solve this challenge. Using a real-world case study of a content recommender, he will demonstrate how to use models like gradient boosting and neural networks to generate relevant recommendations from day one.
Understand the fundamental types of recommender systems (e.g., collaborative filtering, content-based) and their limitations.
Discover practical strategies and architectures for solving cold start scenarios.
Learn takeaways in using gradient boosting and neural networks in recommender systems
Gain insights into the challenges and best practices of deploying a recommender system into a production environment.
This session will provide the blueprint for moving beyond textbook examples to production-ready recommendation engines that are effective from day one
