Towards Web-scale Recommendations with LLMs: From Quality-aware Ranking to Candidate Generation

Explore Further @ Bing is a webpage-to-webpage recommendation product, enhancing the search experience on Bing by surfacing engaging webpage recommendations tied to the search result URLs. In this paper, we present our approach for leveraging Large Language Models (LLMs) for enhancing our web-scale recommendation system. We describe the development and validation of our LLM-powered recommendation quality metric RecoDCG. We discuss our core techniques for utilizing LLMs to make our ranking stage quality-aware. Furthermore, we detail Q' recall, a recall path that enhances our system's candidate generation stage by leveraging LLMs to produce complementary and engaging recommendation candidates. We also address how we optimize our system for multiple objectives, balancing recommendation quality with click metrics. We deploy our work to production, achieving a significant improvement in recommendation quality. We share results from offline and online experiments as well as insights and steps we took to ensure our approaches scale effectively for our web-scale needs.