In this episode, we will discuss how Expedia’s recommendation system is designed to handle both standard destination searches and property-specific searches. While traditional ranking models optimize for broad search behavior, Expedia’s team refines their learning-to-rank approach by integrating property similarity, ensuring travelers get recommendations that align with their intent.

For more details, you can refer to their published tech blog, linked here for your reference: https://medium.com/expedia-group-tech/learning-to-rank-at-expedia-group-how-to-adapt-the-property-search-result-page-based-on-f4ebef78c94b

In this episode, we will explore why evaluating LLM-based chatbots is critical for businesses, the limitations of traditional evaluation methods, and what could be a good robust evaluation framework covering both search performance and LLM-specific metrics.
For more details, you can refer to their published tech blog, linked here for your reference: https://medium.com/data-science-at-microsoft/evaluating-llm-based-chatbots-a-comprehensive-guide-to-performance-metrics-9c2388556d3e

In this episode, we will explore how The New York Times balances algorithmic recommendations with editorial judgment. We discuss their business challenge, examine their hybrid content recommendation system, and look at refinements designed to improve the reader experience.

For more details, you can refer to their published tech blog, linked here for your reference: https://open.nytimes.com/how-the-new-york-times-incorporates-editorial-judgement-in-algorithms-to-curate-home-screen-content-85f48209fdad