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Collaborative search engine

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Models of Collaboration
– Intent (explicit and implicit)
– Synchronization
– Depth of mediation
– Task vs. trait
– Division of labor and sharing of knowledge

Explicit vs. Implicit Collaboration
– Implicit collaboration examples: I-Spy, Jumper 2.0, Seeks, Community Search Assistant, CSE of Burghardt et al., works of Longo et al.
– Systems identify similar users, queries, and links automatically, and recommend related queries and links
– Explicit collaboration examples: SearchTogether, PlayByPlay, Reddy et al.’s MUSE and MUST, Cerciamo
– Users share an agreed-upon information need and work together towards that goal
– Papagelis et al. combine explicitly shared links and implicitly collected browsing histories to a hybrid CSE

Community of Practice
– Sharing search experiences among users with similar interests reduces effort in retrieving desired information
– Collaborative search within a community of practice indexes and ranks search results based on learned preferences
– Users benefit from sharing information, experiences, and awareness to personalize result-lists
– Community represents users with common interests and professions
– Example: ApexKB (previously known as Jumper 2.0)

Depth of Mediation
– Depth of mediation refers to the degree of CSE’s involvement in search
– UI-level mediation examples: SearchTogether, PlayByPlay, Cerchiamo
– Algorithmic mediation examples: I-Spy, recommendation systems
– UI-level mediation focuses on exchanging query results and judgments of relevance
– Algorithmic mediation uses individuals’ search activity to affect search results

Platforms and Modalities
– CSEs initially started on desktop browsers
– Examples: GroupWeb, SearchTogether, CoSense
– CSEs now take advantage of mobile phones and tablets
– Examples: CoSearch, PlayByPlay
– CSEs support co-located collaborative web search and browsing between mobile and desktop users

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.

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