Event detection from evolution of click-through data

Previous efforts on event detection from the web have focused primarily on web content and structure data ignoring the rich collection of web log data. In this paper, we propose the first approach to detect events from the click-through data, which is the log data of web search engines. The intuition behind event detection from click-through data is that such data is often event-driven and each event can be represented as a set of query-page pairs that are not only semantically similar but also have similar evolution pattern over time. Given the click-through data, in our proposed approach, we first segment it into a sequence of bipartite graphs based on the user-defined time granularity. Next, the sequence of bipartite graphs is represented as a vectorbased graph, which records the semantic and evolutionary relationships between queries and pages. After that, the vector-based graph is transformed into its dual graph, where each node is a query-page pair that will be used to represent...

• Type: Conference
• Year: 2006
• Where: KDD
• Authors: Qiankun Zhao, Tie-Yan Liu, Sourav S. Bhowmick, Wei-Ying Ma
• Keywords: Click-through Data, Data Mining, KDD 2006, Real Click-through Data, Vector-based Graph