Query similarity by projecting the query-flow graph

Defining a measure of similarity between queries is an interesting and difficult problem. A reliable query-similarity measure can be used in a variety of applications such as query recommendation, query expansion, and advertising. In this paper, we exploit the information present in query logs in order to develop a measure of semantic similarity between queries. Our approach relies on the concept of the query-flow graph, a graph-based representation of a query log. The query-flow graph aggregates query reformulations from many users: nodes in the graph represent queries, and two queries are connected if they are likely to appear as part of the same search goal. Our query-similarity measure is obtained by projecting the graph (or appropriate subgraphs extracted from it) on a low-dimensional Euclidean space. Our experiments show that the measure we obtain captures a notion of semantic similarity between queries and it is useful for diversifying query recommendations. Categories and S...

• Type: Conference
• Year: 2010
• Where: SIGIR
• Authors: Ilaria Bordino, Carlos Castillo, Debora Donato, Aristides Gionis
• Keywords: Information Management, Query Log, Query Recommendations, Query-similarity Measure, SIGIR 2010