Extracting and Clustering Related Keywords based on History of Query Frequency

Abstract

Query-recommendation systems based on inputted queries have become widespread. These services are effective if users cannot input relevant queries. However, the conventional systems do not take into consideration the relevance between recommended queries. This paper proposes a method of obtaining related queries and clustering them by using the history of query frequencies in query logs. We define similarity in queries based on the history of query frequency and use it for clustering queries. We selected various queries and extracted related queries and then clustered them. We found that our method was useful for clustering queries that were used in around the same term.