Collaborative Web Search Utilizing Experts' Experiences

Abstract

Collaborative Web search improves search quality by users' working in cooperation and is a subset of social search. Current Web browsers and search engines provide limited support for it. However, it is easier for experts, who are familiar with some topics, fulfill they needs through search engines due their backgrounds, domain knowledge and so on. A sharing experts’ experiences approach should be struck based on today's Web browsers and major search engines. This paper presents a convenient way for users share and utilize experts' experiences through a Web browser toolbar for collaborative Web search. The toolbar an catch search histories and favorites and display recommendations for every user in a popular Web browser through integrating with mainstream search engines like Google, Yahoo!, et al. These collected users' data are uploaded a recommendation server, in which recommendations are built according some rules based on an utilizing experts' experiences approach. The toolbar can download some valuable recommendations merging into default search list for prompting a searcher. The core of our proposed approach is a scalable method measure to what degree a user is an expert for a given topic and detect an expert's experiences based on a hierarchical user profile. Experiments showed that the novel collaborative Web search way is acceptant users and experts' experiences improved search quality when compared standard Google rankings. More importantly, results verified our hypothesis that a significant improvement on search quality can be achieved by utilizing experts' experiences.