Collaborative search engine

Collaborative search engines (CSE) are Web search engines and enterprise searches within company intranets that let users combine their efforts in information retrieval (IR) activities, share information resources collaboratively using knowledge tags, and allow experts to guide less experienced people through their searches. Collaboration partners do so by providing query terms, collective tagging, adding comments or opinions, rating search results, and links clicked of former (successful) IR activities to users having the same or a related information need.

Collaborative search engines can be classified along several dimensions: intent (explicit and implicit) and synchronization and depth of mediation , task vs. trait, and division of labor and sharing of knowledge.

Implicit collaboration characterizes Collaborative filtering and recommendation systems in which the system infers similar information needs. I-Spy,Jumper 2.0, Seeks, the Community Search Assistant, the CSE of Burghardt et al., and the works of Longo et al. all represent examples of implicit collaboration. Systems that fall under this category identify similar users, queries and links clicked automatically, and recommend related queries and links to the searchers.

Explicit collaboration means that users share an agreed-upon information need and work together toward that goal. For example, in a chat-like application, query terms and links clicked are automatically exchanged. The most prominent example of this class is SearchTogether published in 2007. SearchTogether offers an interface that combines search results from standard search engines and a chat to exchange queries and links. Reddy et al. (2008) follow a similar approach and compares two implementations of their CSE called MUSE and MUST. Reddy et al. focuses on the role of communication required for efficient CSEs. Representatives for the class of implicit collaboration are I-Spy, the Community Search Assistant, and the CSE of Burghardt et al. Cerciamo supports explicit collaboration by allowing one person to concentrate on finding promising groups of documents, while having the other person make in-depth judgments of relevance on documents found by the first person.

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