Collaborative search using an implicitly formed academic network

The purpose of this paper is to propose the Collaborative Search System that attempts to achieve collaboration by implicitly identifying and reflecting search behaviour of collaborators in an academic network that is automatically and dynamically formed. By using the constructed Collaborative Hit Matrix (CHM), results are obtained that are based on the search behaviour and earned preferences of specialist communities of researchers, which are relevant to the user's need and reduce the time spent on bad links.

By using the Digital Bibliography Library Project (DBLP), the research communities are formed implicitly and dynamically based on the users’ research presence in the search environment and in the publication scenario, which is also used to assign users’ roles and establish links between the users. The CHM, to store the hit count and hit list of page results for queries, is also constructed and updated after every search session to enhance the collaborative search among the researchers.

The implicit researchers community formation, the assignment and dynamic updating of roles of the researchers based on research, search presence and search behaviour on the web as well as the usage of these roles during Collaborative Web Search have highly improved the relevancy of results. The CHM that holds the collaborative responses provided by the researchers on the search query results to support searching distinguishes this system from others. Thus the proposed system considerably improves the relevancy and reduces the time spent on bad links, thus improving recall and precision.

The research findings illustrate the better performance of the system, by connecting researchers working in the same field and allowing them to help each other in a web search environment.