In my upcoming talk, I will explore methods to boost the trustworthiness of recommendation systems, focusing on our Brickmaster system, alongside our detailed model evaluation framework involving both offline and online strategies. Offline evaluations leverage historical data to verify that the model satisfies all set performance and reliability standards before deployment. Online evaluations, on the other hand, provide continual real-time feedback essential for enhancing the system's effectiveness. Additionally, I will cover the integration of Large Language Models (LLMs) into our system, highlighting how this has markedly improved the accuracy and personalization of our recommendations, thereby boosting overall system performance. This presentation aims to demonstrate how cutting-edge AI technologies and systematic evaluations drive the development of strong, dependable, and efficient recommendation systems in our increasingly data-centric world.

Understand the intricacies of modern recommendation systems and how to achieve trustworthiness to meet real business objectives. Additionally, learn how Large Language Models (LLMs) can enhance the effectiveness of recommendation systems, providing deeper insights and improved performance.