"More to Read" at the Los Angeles Times: Solving a Cold Start Problem with LLMs to Improve Story Discovery

News publishers, who are seeking to grow their digital audience, face a challenge in providing relevant content recommendations for unregistered users arriving directly to article pages. In these cold start scenarios, classic techniques, like asking a user to register and select topics of interest, fall short. We present a contextual targeting approach that leverages the user’s current article choice as an implicit signal of user interests. We designed and developed an interface with recommendations to help users discover more articles. Our A/B testing showed that our models increased click-through rates by 39.4% over a popularity baseline. One of them, a large language model (LLM), generates relevant recommendations that balance immersion and novelty. We discuss the implications of using LLMs for responsibly enhancing user experiences while upholding editorial standards. We identify key opportunities in detecting nuanced user preferences and identifying and interrupting filter bubbles on news publisher sites.