Collaborative search engine

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