Contextual Query Intent Extraction for Paid Search Selection

Paid Search algorithms play an important role in online advertising where a set of related ads is returned based on a searched query. The Paid Search algorithms mostly consist of two main steps. First, a given searched query is converted to different sub-queries or similar phrases which preserve the core intent of the query. Second, the generated sub-queries are matched to the ads bidded keywords in the data set, and a set of ads with highest utility measuring relevance to the original query are returned. The focus of this paper is optimizing the first step by proposing a contextual query intent extraction algorithm to generate sub-queries online which preserve the intent of the original query the best. Experimental results over a very large real-world data set demonstrate the superb performance of proposed approach in optimizing both relevance and monetization metrics compared with one of the existing successful algorithms in our system.