Improving the presentation of search results by multipartite graph clustering of multiple reformulated queries and a novel document representation

The goal of clustering web search results is to reveal the semantics of the retrieved documents. The main challenge is to make clustering partition relevant to a user’s query. In this paper, we describe a method of clustering search results using a similarity measure between documents retrieved by multiple reformulated queries. The method produces clusters of documents that are most relevant to the original query and, at the same time, represent a more diverse set of semantically related queries. In order to cluster thousands of documents in real time, we designed a novel multipartite graph clustering algorithm that has low polynomial complexity and no manually adjusted hyper–parameters. The loss of semantics resulting from the stem–based document representation is a common problem in information retrieval. To address this problem, we propose an alternative novel document representation, under which words are represented by their synonymy groups.