Lessons Learned from Building a Large-Scale Recommendation System for Headspace
 

Keynote Panel : Advancement in Recommendation Systems

Headspace is a leading mindful and meditation app with over 60 million users worldwide. In order to provide the best possible experience for our users, we built a large-scale recommendation system that helps them discover new content that is relevant to their interests. In this talk, we will share the lessons we have learned from building and operating this recommendation system. We will discuss the challenges we faced, the solutions we implemented, and the results we achieved. We will also discuss the future of recommendation systems and how they can be used to improve the user experience of other applications. This talk is relevant to researchers, data scientists, and business professionals who are interested in the latest advances in recommender systems. Here are some specific lessons that we have learned from building this recommendation system: Data is king: The quality and quantity of data is essential for building a successful recommendation system. Algorithms matter: The right algorithms can make a big difference in the accuracy and effectiveness of a recommendation system. Personalization is key: Users are more likely to engage with content that is relevant to their interests. Continuous learning is essential: Recommendation systems need to be constantly updated with new data and feedback in order to stay accurate and effective. We hope that this talk will provide you with valuable insights into the challenges and opportunities of building large-scale recommendation systems.
Who is this presentation for?
Data Scientist, Machine Learning Engineer
Prerequisite knowledge:
Intermediate Knowledge of Machine Learning and Cloud Engineering
What you'll learn?
System Design for Recommender System