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