Constructing a Multidimensional Topic-Concept Cube for OLAP on Large-Scale Search Logs

In addition to search queries and the corresponding click-through information, search engine logs record multidimensional information about user search activities, such as search time, location, vertical, and search device. Multidimensional mining of search logs can provide novel insights and useful knowledge for both search engine users and developers. In this paper, we describe our topic-concept cube project, which addresses the business need of supporting multidimensional mining of search logs effectively and efficiently. We answer two challenges. First, search queries and click-through data are well recognized sparse, and thus have to be aggregated properly for effective analysis. Second, there is often a gap between the topic hierarchies in multidimensional aggregate analysis and queries in search logs. To address those challenges, we develop a novel topic-concept model that learns a hierarchy of concepts and topics automatically from search logs. Enabled by the topic-concept model, we construct a topic-concept cube that supports online multidimensional mining of search log data. A distinct feature of our approach is that, in addition to the standard dimensions such as time and location, our topic-concept cube has a dimension of topics and concepts, which substantially facilitates the analysis of log data. To handle a huge amount of log data, we develop distributed algorithms for learning model parameters efficiently. We also devise approaches to computing a topic-concept cube. We report an empirical study verifying the effectiveness and efficiency of our approach on a real data set of 1.96 billion queries and 2.73 billion clicks.