QAque

Abstract

Recently, query suggestions have become quite useful in web searches. Most provide additional and correct terms based on the initial query entered by users. However, query suggestions often recommend queries that differ from the user's search intentions due to different contexts. In such cases, faceted query expansions and their usages are quite efficient. In this paper, we propose faceted query expansion methods using the resources of Community Question Answering (CQA), which is social network service (SNS) that shares user knowledge. In a CQA site, users can post questions in a suitable category. Others answer them based on the framework. Thus, the CQA category makes a facet of the query expansion. In addition, the time of year when the question was posted plays an important role in understanding its context. Thus, such seasonality creates another facet of the query expansion. We implement two-dimensional faceted query expansion methods based on the results of the Latent Dirichlet Allocation (LDA) analysis of CQA resources. The question articles deriving query expansion are provided for choosing appropriate terms by users. Our sophisticated evaluations using actual and long-term CQA resources, such as Yahoo! CHIEBUKURO, demonstrate that most parts of the CQA questions are posted in periodicity and in bursts.