Learning search tasks in queries and web pages via graph regularization

As the Internet grows explosively, search engines play a more and more important role for users in effectively accessing online information. Recently, it has been recognized that a query is often triggered by a search task that the user wants to accomplish. Similarly, many web pages are specifically designed to help accomplish a certain task. Therefore, learning hidden tasks behind queries and web pages can help search engines return the most useful web pages to users by task matching. For instance, the search task that triggers query “thinkpad T410 broken” is to maintain a computer, and it is desirable for a search engine to return the Lenovo troubleshooting page on the top of the list. However, existing search engine technologies mainly focus on topic detection or relevance ranking, which are not able to predict the task that triggers a query and the task a web page can accomplish. In this paper, we propose to simultaneously classify queries and web pages into the popular searc...

• Type: Journal
• Year: 2011
• Where: SIGIR
• Authors: Ming Ji, Jun Yan, Siyu Gu, Jiawei Han, Xiaofei He, Wei Vivian Zhang, Zheng Chen
• Keywords: Information Storage And Retrieval, Information Technology, Search Engine Technologies, Search Task, SIGIR 2011