We are proposing a new similarity based recommendation system for large-scale dynamic marketplaces. Our solution consists of an offline process, which generates long-term cluster definitions grouping short-lived item listings, and an online system, which utilizes these clusters to first focus on important similarity dimensions and next conducts a trade-off between further similarity and other quality factors such as seller trustworthiness. Our system generates these clusters from several hundred millions of item listings using a large Hadoop map-reduce based system. The clusters are learned using user queries as the main information source and therefore biased towards how users conceptually group items. Our system is deployed on several eBay sites in large-scale and has increased user-engagement and business metrics compared to the previous system. We show that utilizing user queries helps capturing similarity better. We also present experiments demonstrating that adapting the ranking function, which controls the trade-off between similarity and quality, to a specific context improves recommendation performance.