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
Our Speaker, Dr. Enrico Fonda is a data scientist with a background in physics. He conducted postdoctoral research at the University of Maryland and New York University, where he studied quantum fluids and applied deep learning to turbulence. In 2019, Enrico moved to London and transitioned his skills to the industry. He has since worked across the MarTech, Telco, and Tech sectors, focusing on machine learning modeling, generative AI applications, and code generation. Today, as a Solution Consultant at Sahaj Software, he continues to solve complex problems in data science and AI.
The evening will feature:
🎤 One engaging talk with Q&A
🤝 Plenty of time for networking over free pizza and non-alcoholic drinks
Don’t miss this chance to learn, connect, and share with fellow tech enthusiasts.
👉 Please click the link below to register.
https://sahaj.ai/events/recommender-systems-in-the-real-world-tackling-the-cold-start-challenge/
