Temporal Query Substitution For Ad Search

Recently, information retrieval researchers have witnessed the increasing interest in query substitution for ad search. Most previous works substitute search queries via content based query similarities, and few of them take the temporal characteristics of queries into consideration. In this extended abstract, we propose a novel temporal similarity measurement for query substitution in ad search task. We firstly extract temporal features, such as burst and periodicity, from query frequency curves and then define the temporal query similarity by integrating these new features with the temporal query frequency distribution. Compared to the traditional temporal similarity measurements such as correlation coefficient, our proposed approach is more effective owing to the explicit extraction of high-level semantic query temporal features for similarity measure. The experimental results demonstrate that the proposed similarity measure can make the ads more relevant to user search queries compared to ad search without temporal features.