Query intent inference via search engine log

摘要

Mining the latent intents behind search queries is critical for contemporary search engines. Therefore, there has been lots of effort on studying how to infer the intents of search queries via search engine query log. However, the task of query log-based intent inference is not trivial, since it involves cross-disciplinary knowledge of data modeling and data mining. In this paper, we tackle the problem of query intent inference by integrating multiple information sources in a seamless manner. We first propose a comprehensive data model called Search Query Log Structure (SQLS) that represents the relation between search queries via the User dimension, the URL dimension, the Session dimension and the Term dimension. In order to explore the effective ways of using such multidimensional information modeled by SQLS, we survey and compare three frameworks, namely Result-Oriented Framework, Laplacian-Oriented Framework and Topic-Oriented Framework, to infer the intents of search queries. Experimental results show that the three frameworks significantly outperform the state-of-the-art approach and meet the diverse requirements arising from different application scenarios.