Machine learning for predicting user clicks in Webbased search offers automated explanation of user activity. We address click prediction in the Web search scenario by introducing...
Ding Zhou, Levent Bolelli, Jia Li, C. Lee Giles, H...
The interfaces used by the top Web search engines have changed very little since the early days of Web search. These interfaces follow the traditional model of information retriev...
Current web search engines essentially conduct document-level ranking and retrieval. However, structured information about realworld objects embedded in static webpages and online...
Despite the success of modern Web search engines, challenges remain when it comes to providing people with access to the right information at the right time. In this paper, we desc...
Collaboration on Web search is common in many domains, such as education and knowledge work; recently, HCI researchers have begun to introduce prototype collaborative search tools...
Learning-to-rank algorithms, which can automatically adapt ranking functions in web search, require a large volume of training data. A traditional way of generating training examp...
Machine Learned Ranking approaches have shown successes in web search engines. With the increasing demands on developing effective ranking functions for different search domains, ...
Keke Chen, Rongqing Lu, C. K. Wong, Gordon Sun, La...
We analyzed the patterns of coordination between users' eye movements and mouse movements when scanning a web search results page, using data gathered from a study with 32 pa...
Throughout many of the different types of Web searches people perform, the primary tasks are to first craft a query that effectively captures their information needs, and then eva...
Online services such as web search, news portals, and ecommerce applications face the challenge of providing highquality experiences to a large, heterogeneous user base. Recent ef...