Defining & Optimizing Quality of LinkedIn's Content Search

Most search engines optimize for quality metrics in addition to engagement metrics. Quality metrics require an explicit definition of the relevance of a document for a query. Defining relevance is a non-trivial problem. Simple definitions such as on-topicness of a document to a query may not adequately capture all aspects of quality, such as document originality, depth and recency. These aspects can have an impact on the value provided by a search engine to searchers, which in-turn drives engagement metrics in the long-term. So, a more thoughtful definition can be very beneficial. However, a complex definition necessitates the need for a search system that has a deep understanding of language. In this paper we present our definition of relevance for content search, and the design of our content search system that enables optimization of Precision based on that definition.