Recommender Systems in the Real World: Tackling the Cold Start Challenge скачать в хорошем качестве

Recommender Systems in the Real World: Tackling the Cold Start Challenge

About the Talk Recommender Systems in the Real World: Tackling the Cold Start Challenge Every recommender system, from Netflix to Spotify, faces the same early hurdle: the cold start problem. How do you provide meaningful recommendations for new users or new items when you have no interaction history? In this DevDay session, Dr. Enrico Fonda, Solution Consultant at Sahaj Software, broke down how real-world systems tackle this challenge. Through a practical case study of a content recommender, he demonstrated how models like gradient boosting and neural networks can generate relevant recommendations from day one, even when data is sparse. 📌 This talk covered: The fundamental types of recommender systems and their limitations Practical strategies for solving user and item cold start problems How gradient boosting and neural networks fit into modern recommender pipelines Challenges and best practices for deploying recommendation engines in production Patterns, architectures, and insights from real-world implementations About the Speaker Dr. Enrico Fonda Solution Consultant, Sahaj Software Enrico is a data scientist with a strong foundation in physics. Before moving into industry, he conducted postdoctoral research at the University of Maryland and New York University, studying quantum fluids and applying deep learning to turbulence. Since relocating to London in 2019, he has worked across MarTech, Telco, and Tech, focusing on machine learning modeling, generative AI applications, and code generation. As a Solution Consultant at Sahaj, he continues to apply deep technical expertise to complex data and AI problems. 🎥 Watch the full talk to learn how real-world recommender systems overcome the cold start challenge and how you can build ones that perform from day one. #devday #SahajSoftware #RecommenderSystems #ColdStartProblem #machinelearning #GradientBoosting #neuralnetworks #MLEngineering #AIForIndustry