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 (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, depth of mediation, task vs. trait, 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. PlayByPlay takes a step further to support general purpose collaborative browsing tasks with an instant messaging functionality. Reddy et al. follow a similar approach and compares two implementations of their CSE called MUSE and MUST. Reddy et al. focus on the role of communication required for efficient CSEs. 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. However, in Papagelis et al. terms are used differently: they combine explicitly shared links and implicitly collected browsing histories of users to a hybrid CSE. Recent work in collaborative filtering and information retrieval has shown that sharing of search experiences among users having similar interests, typically called a community of practice or community of interest, reduces the effort put in by a given user in retrieving the exact information of interest. Collaborative search deployed within a community of practice deploys novel techniques for exploiting context during search by indexing and ranking search results based on the learned preferences of a community of users. The users benefit by sharing information, experiences and awareness to personalize result-lists to reflect the preferences of the community as a whole. The community representing a group of users who share common interests, similar professions. The best known example is the open-source project ApexKB (previously known as Jumper 2.0). The depth of mediation refers to the degree that the CSE mediates search. SearchTogether is an example of UI-level mediation: users exchange query results and judgments of relevance, but the system does not distinguish among users when they run queries. PlayByPlay is another example of UI-level mediation where all users have full and equal access to the instant messaging functionality without the system's coordination. Cerchiamo and recommendation systems such as I-Spy keep track of each person's search activity independently and use that information to affect their search results. These are examples of deeper algorithmic mediation. This model classifies people's membership in groups based on the task at hand vs. long-term interests; these may be correlated with explicit and implicit collaboration.