While some web search users know exactly what they are looking for, others are willing to explore other topics related to an initial interest. Often, the user's initial interest can be uniquely linked to an entity in a knowledge base, and in this case it is natural to recommend the explicitly linked entities for further exploration. In real world knowledge bases, however, the number of linked entities may be very large and not all related entities may be equally relevant. Thus there is a need for ranking related entities. In this paper, we describe Spark, a recommendation engine that links a users' initial query to an entity within a knowledge base and provides a ranking of the related entities. Spark extracts several signals from a variety of data sources, including user sessions, Twitter and Flickr, using a large cluster of computers running Hadoop. These signals are combined with a machine-learned ranking model in order to produce a final recommendation of entities to user queries, which is currently powering Yahoo Search results pages.