Collaborative search engine

Models of collaboration

Collaborative search engines can be classified along several dimensions: intent (explicit and implicit) and synchronization,{{citation | contribution-url = http://workshops.fxpal.com/jcdl2008/submissions/tmpDF.pdf | title-link = Joint Conference on Digital Libraries | access-date = 2009-07-30 | archive-url = https://web.archive.org/web/20110716030924/http://www.computing.dcu.ie/~cfoley/cfoley-PhD_thesis.pdf | archive-date = 2011-07-16 | url-status = dead

Explicit vs. implicit collaboration

Implicit collaboration characterizes Collaborative filtering and recommendation systems in which the system infers similar information needs. I-Spy,{{citation 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{{Cite book| year = 2007

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.

Community of practice

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.{{citation

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.{{citation | editor4-first = Eelco | editor3-first = Pearl | editor2-first = Judy | editor1-first = Wolfgang | editor1-last = Nejdl | name-list-style = amp | editor2-last = Kay | editor4-last = Herder | editor3-last = Pu| display-editors = 3| citeseerx = 10.1.1.153.7573 | access-date = 2012-05-16 | archive-url = https://web.archive.org/web/20120604045016/http://www.trilexnet.com/labs/jumper | archive-date = 2012-06-04 | url-status = dead

Depth of mediation

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.

Task vs. trait

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.

Platforms and modalities

CSE systems started off on the desktop end, with the earliest ones being extensions or modifications to existing web browsers. GroupWeb{{citation | doi-access = free

With the prevalence of mobile phones and tablets, CSEs are also taking advantage of these additional device modalities. CoSearch{{citation

Synchronous vs. asynchronous collaboration

Synchronous collaboration model enables different users to work toward the same goal together simultaneously, with each individual user having access to one another's progress in real-time. A typical example of the synchronous collaboration model is GroupWeb, where users are made aware of what others are doing through features such as synchronous scrolling with pages, telepointers for enacting gestures, and group annotations that are attached to web pages.

Asynchronous collaboration models offer more flexibility toward when different users' different search processes are carried out while reducing the cognitive effort for later users to consume and build upon previous users' search results. SearchTogether, for example, supports asynchronous collaboration functionalities by persisting previous users' chat logs, search queries, and web browsing histories so that the later users could quickly bring themselves up to speed.

Applications of collaborative search engines

The applications of CSEs are well-explored in both the academic community and industry. For example, GroupWeb was used as a presentation tool for real-time distance education and conferences. ClassSearch{{citation | author5-link = Nathalie Henry Riche

Privacy-aware collaborative search engines

Search terms and links clicked that are shared among users reveal their interests, habits, social relations and intentions.{{citation | name-list-style = amp

As CSEs are a new technology just entering the market, identifying user privacy preferences and integrating Privacy enhancing technologies (PETs) into collaborative search are in conflict. On the one hand, PETs have to meet user preferences, on the other hand, one cannot identify these preferences without using a CSE, i.e., implementing PETs into CSEs. Today, the only work addressing this problem comes from Burghardt et al.{{citation

References

References

1. Athanasios Papagelis. (2007). "Eighth Mexican International Conference on Current Trends in Computer Science (ENC 2007)".