Actively predicting diverse search intent from user browsing behaviors

This paper is concerned with actively predicting search intent from user browsing behavior data. In recent years, great attention has been paid to predicting user search intent. However, the prediction was mostly passive because it was performed only after users submitted their queries to search engines. It is not considered why users issued these queries, and what triggered their information needs. According to our study, many information needs of users were actually triggered by what they have browsed. That is, after reading a page, if a user found something interesting or unclear, he/she might have the intent to obtain further information and accordingly formulate a search query. Actively predicting such search intent can benefit both search engines and their users. In this paper, we propose a series of technologies to fulfill this task. First, we extract all the queries that users issued after reading a given page from user browsing behavior data. Second, we learn a model to e�...

• Type: Conference
• Year: 2010
• Where: WWW
• Authors: Zhicong Cheng, Bin Gao, Tie-Yan Liu
• Keywords: Internet Technology, Search Engine, Search Intent, User Browsing Behavior, WWW 2010