While some web search users know exactly what they are looking for, others are willing to explore topics related to an initial interest. Often, the user's initial interest can be uniquely linked to an entity in a knowledge base. 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 user's 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 Yahoo! Web Search, 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. This system is currently powering Yahoo! Web Search result pages.
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机译：尽管某些网络搜索用户确切知道他们在寻找什么，但其他人却愿意探索与最初兴趣相关的主题。通常，用户的最初兴趣可以唯一地链接到知识库中的实体。在这种情况下，很自然地推荐显式链接的实体进行进一步的探索。但是，在现实世界的知识库中，链接实体的数量可能非常大，并且并非所有相关实体都具有同等相关性。因此，需要对相关实体进行排名。在本文中，我们描述了Spark，这是一个推荐引擎，它将用户的初始查询链接到知识库中的一个实体，并提供相关实体的排名。 Spark从包括Yahoo!在内的各种数据源中提取了几种信号。 Web Search，Twitter和Flickr，使用运行Hadoop的大型计算机集群。这些信号与机器学习的排名模型结合在一起，以生成实体对用户查询的最终推荐。该系统目前正在为Yahoo!提供动力。网页搜索结果页面。
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