Understanding temporal intent of user query based on time-based query classification

Abstract

Web queries are time sensitive which implies that user's intent for information changes over time. How to recognize temporal intents behind user queries is crucial towards improving the performance of search engines. However, to the best of our knowledge, this problem has not been studied in existing work. In this paper, we propose a timebased query classification approach to understand user's temporal intent automatically. We first analyzed the shared features of queries' temporal intent distributions. Then, we present a query taxonomy which group queries according to their temporal intents. Finally, for a new given query, we propose a machine learning method to decide its class in terms of its search frequency over time recorded in Web query logs. Experiments demonstrate that our approach can understand users' temporal intents effectively. © Springer-Verlag Berlin Heidelberg 2013.