Analyzing User Preferences and Quality Improvement on Bing's WebPage Recommendation Experience with Large Language Models

Explore Further @ Bing (Web Recommendations) is a web-scale query independent webpage-to-webpage recommendation system with an index size of over 200 billion webpages. Due to the significant variability in webpage quality across the web and the reliance of our system on learning soleley user behavior (clicks), our production system was susceptible to serving clickbait and low-quality recommendations. Our team invested several months in developing and shipping several improvements that utilize LLM-generated recommendation quality labels to enhance our ranking stack to improve the nature of the recommendations we show to our users. Another key motivation behind our efforts was to go beyond merely surfacing relevant webpages, focusing instead on prioritizing more useful and authoritative content that delivers value to users based on their implied intent. We demonstrate how large language models (LLMs) offer a powerful tool for product teams to gain deeper insights into shifts in product experience and user behavior following significant improvements or changes to a production system. In this work, to enable our analysis, we also showcase the use of a small language model (SLM) to generate better-quality webpage text features and summaries at scale and describe our approach to mitigating position bias in user interaction logs."