Collaborative search engine

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title: "Collaborative search engine" type: doc version: 1 created: 2026-02-28 author: "Wikipedia contributors" status: active scope: public tags: ["information-retrieval-systems"] topic_path: "general/information-retrieval-systems" source: "https://en.wikipedia.org/wiki/Collaborative_search_engine" license: "CC BY-SA 4.0" wikipedia_page_id: 0 wikipedia_revision_id: 0

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.

Models of collaboration

Collaborative search engines can be classified along several dimensions: intent (explicit and implicit) and synchronization,{{citation | title = Collaborative Exploratory Search | year = 2007 | author = Golovchinsky Gene | author2 = Pickens Jeremy | journal = Proceedings of HCIR 2007 Workshop | url = http://projects.csail.mit.edu/hcir/web/hcir07.pdf | year = 2008 | author = Pickens Jeremy | author2 = Golovchinsky Gene | author3 = Shah Chirag | author4 = Qvarfordt Pernilla | author5 = Back Maribeth | title = SIGIR '08: Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval | pages = 315–322 | doi = 10.1145/1390334.1390389 | isbn = 9781605581644 | chapter = Algorithmic mediation for collaborative exploratory search | s2cid = 15704152 | contribution = Understanding Groups’ Properties as a Means of Improving Collaborative Search Systems | year = 2008 | author = Morris Meredith | author2 = Teevan Jaime | title = 1st International Workshop on Collaborative Information Retrieval, held in conjunction with JCDL 2008 | contribution-url = http://workshops.fxpal.com/jcdl2008/submissions/tmpDF.pdf | title-link = Joint Conference on Digital Libraries | degree=PhD | title = Division of Labour and Sharing of Knowledge for Synchronous Collaborative Information Retrieval | year = 2008 | last= Foley | first= Colum | publisher = Dublin City University | url = http://www.computing.dcu.ie/~cfoley/cfoley-PhD_thesis.pdf | 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 | title = Collaborative Web Search | year = 2003 | author = Barry Smyth | author2 = Evelyn Balfe | author3 = Peter Briggs | author4 = Maurice Coyle | author5 = Jill Freyne | journal = IJCAI | pages = 1417–1419 | title = Community search assistant | year = 2001 | author = Natalie S. Glance | journal = Workshop on AI for Web Search AAAI'02 | author = Thorben Burghardt | author2 = Erik Buchmann | author3 = Klemens Böhm | title = 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology | chapter = Discovering the Scope of Privacy Needs in Collaborative Search | pages = 910–913 | doi = 10.1109/WIIAT.2008.165 | isbn = 978-0-7695-3496-1| s2cid = 15921662 | title = Toward Social Search - From Explicit to Implicit Collaboration to Predict Users' Interests | year = 2009 | author = Longo Luca | author2 = Barrett Stephen | author3 = Dondio Pierpaolo | journal = Webist 2009 - Proceedings of the Fifth International Conference on Web Information Systems and Technologies, Lisbon, Portugal, March 23–26, 2009 | pages = 693–696 | volume = 1 | isbn = 978-989-8111-81-4 | author = Longo Luca | author2 = Barrett Stephen | author3 = Dondio Pierpaolo | title = Transactions on Computational Collective Intelligence II | chapter = Enhancing Social Search: A Computational Collective Intelligence Model of Behavioural Traits, Trust and Time | pages = 46–69 | volume = 2 | doi = 10.1007/978-3-642-17155-0_3 | isbn = 978-3-642-17154-3| series = Lecture Notes in Computer Science | bibcode = 2010LNCS.6450...46L | title = Information Foraging Theory as a Form of Collective Intelligence for Social Search | year = 2009 | author = Longo Luca | author2 = Barrett Stephen | author3 = Dondio Pierpaolo | journal = Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems, First International Conference, ICCCI 2009, Wroclaw, Poland, October 5–7, 2009. Proceedings | pages = 63–74 | volume = 1 | isbn = 978-3-642-04440-3 | url = http://dl.acm.org/citation.cfm?id=1692026 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 | author = Meredith Ringel Morris | author2 = Eric Horvitz | title = Proceedings of the 20th annual ACM symposium on User interface software and technology | chapter = SearchTogether: An interface for collaborative web search | pages = 3–12 | doi = 10.1145/1294211.1294215 | isbn = 9781595936790 | s2cid = 10783726 | url = http://portal.acm.org/citation.cfm?id=1294211.1294215 | author2 = Jeffrey Nichols | title = Proceedings of the SIGCHI Conference on Human Factors in Computing Systems | chapter = CoSearch: A system for co-located collaborative web search | date = 2008 | pages = 1647–1656 | doi = 10.1145/1357054.1357311 | isbn = 9781605580111 | s2cid = 9854331 | url = https://dl.acm.org/citation.cfm?doid=1357054.1357311 | title = The Role of Communication in Collaborative Information Searching | year = 2008 | author = Madhu C. Reddy | author2 = Bernhard J. Jansen | author3 = Rashmi Krishnappa | journal = ASTIS

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 | title = A Collaborative Filtering based Re-ranking Strategy for Search in Digital Libraries | year = 2002 | author = Rohini U | author2 = Vamshi Ambati | journal = ICADL2005: The 8th International Conference on Asian Digital Libraries | url = http://www.aaai.org/Papers/Workshops/2006/WS-06-10/WS06-10-004.pdf }}

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 | year = 2008 | editor1-last = Nejdl | author = Maurice Coyle | author2 = Barry Smyth | title = Adaptive Hypermedia and Adaptive Web-Based Systems | series = Lecture Notes in Computer Science | name-list-style = amp | pages = 103–112 | volume = 5149 | doi = 10.1007/978-3-540-70987-9 | isbn = 978-3-540-70984-8 | url = http://portal.acm.org/citation.cfm?id=1485050 | editor2-last = Kay | editor4-last = Herder | editor3-last = Pu| display-editors = 3| citeseerx = 10.1.1.153.7573 | title = Jumper Networks Releases Jumper 2.0.1.5 Platform with New Community Search Features | year = 2010 | author = Jumper Networks Inc. | journal = Press Release | url = http://www.trilexnet.com/labs/jumper | 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 | title = GroupWeb: A WWW Browser As Real Time Groupware | year = 1996 | author = Saul Greenberg | author2 = Mark Roseman | journal = | doi = 10.1145/257089.257317 | s2cid = 30982523 | doi-access = free | title = Proceedings of the SIGCHI Conference on Human Factors in Computing Systems | year = 2009 | author = Sharoda A. Paul | author2 = Meredith Ringel Morris | chapter = CoSense: Enhancing sensemaking for collaborative web search | journal = | pages = 1771–1780 | doi = 10.1145/1518701.1518974 | isbn = 978-1-60558-246-7 | s2cid = 10280059

With the prevalence of mobile phones and tablets, CSEs are also taking advantage of these additional device modalities. CoSearch{{citation | title = Proceedings of the SIGCHI Conference on Human Factors in Computing Systems | year = 2008 | author = Saleema Amershi | author2 = Meredith Ringel Morris | chapter = CoSearch: A system for co-located collaborative web search | journal = | pages = 1647–1656 | doi = 10.1145/1357054.1357311 | isbn = 978-1-60558-011-1 | s2cid = 9854331

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 | title = Proceedings of the SIGCHI Conference on Human Factors in Computing Systems | year = 2011 | author = Neema Moraveji | author2 = Meredith Ringel Morris | author3 = Daniel Morris | author4 = Mary Czerwinski | author5 = Nathalie Henry Riche | chapter = ClassSearch: Facilitating the development of web search skills through social learning | author5-link = Nathalie Henry Riche | journal = | pages = 1797–1806 | doi = 10.1145/1978942.1979203 | isbn = 978-1-4503-0228-9 | s2cid = 6816313

Privacy-aware collaborative search engines

Search terms and links clicked that are shared among users reveal their interests, habits, social relations and intentions.{{citation | title = Article 29 EU Data Protection Working Party | year = 2008 | author = Data Protection Working Party | journal = EU | title = A Live-User Evaluation of Collaborative Web Search | year = 2005 | author = Barry Smyth | author2 = Evelyn Balfe | author3 = Oisin Boydell | author4 = Keith Bradley | author5 = Peter Briggs | author6 = Maurice Coyle | author7 = Jill Freyne | journal = IJCAI | title = Anonymous personalization in collaborative web search | year = 2005 | author = Smyth, Barry | author2 = Balfe, Evelyn | name-list-style = amp | journal = Inf. Retr. | pages = 165–190 | volume = 9 | issue = 2| doi = 10.1007/s10791-006-7148-z | s2cid = 11659895 | title = Applying Collaborative Filtering for Efficient Document Search | year = 2004 | author = Seikyung Jung | author2 = Juntae Kim | author3 = Herlocker, JL | journal = Inf. Retr. | pages = 640–643

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 | title = Collaborative Search And User Privacy: How Can They Be Reconciled? | year = 2008 | author = Thorben Burghardt | author2 = Erik Buchmann | author3 = Klemens Böhm | author4 = Chris Clifton | journal = CollaborateCom | url = http://dbis.ipd.uni-karlsruhe.de/1184.php

References

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

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