Exploratory search

Last updated

Exploratory search is a specialization of information exploration which represents the activities carried out by searchers who are: [1]

Contents

Exploratory search is distinguished from known-item search, for which the searcher has a particular target in mind.

Consequently, exploratory search covers a broader class of activities than typical information retrieval, such as investigating, evaluating, comparing, and synthesizing, where new information is sought in a defined conceptual area; exploratory [2] data analysis is another example of an information exploration activity. Typically, therefore, such users generally combine querying and browsing strategies to foster learning and investigation.

History

Exploratory search is a topic that has grown from the fields of information retrieval and information seeking but has become more concerned with alternatives to the kind of search that has received the majority of focus (returning the most relevant documents to a Google-like keyword search). The research is motivated by questions like "What if the user doesn't know which keywords to use?" or "What if the user isn't looking for a single answer?" Consequently, research has begun to focus on defining the broader set of information behaviors in order to learn about the situations when a user is, or feels, limited by only having the ability to perform a keyword search.

In the last few years,[ when? ] a series of workshops has been held at various related and key events. In 2005, the Exploratory Search Interfaces workshop focused on beginning to define some of the key challenges in the field. [2] Since then a series of other workshops has been held at related conferences: Evaluating Exploratory Search [3] at SIGIR06 [4] and Exploratory Search and HCI [5] at CHI07 [6] (in order to meet with the experts in human–computer interaction).

In March 2008, an Information Processing and Management special issue [7] [8] focused particularly on the challenges of evaluating exploratory search, given the reduced assumptions that can be made about scenarios of use.

In June 2008, the National Science Foundation sponsored an invitational workshop to identify a research agenda for exploratory search and similar fields for the coming years. [9]

Research challenges

Important scenarios

With the majority of research in the information retrieval community focusing on typical keyword search scenarios, one challenge for exploratory search is to further understand the scenarios of use for when keyword search is not sufficient. An example scenario, often used to motivate the research by mSpace, [10] states: if a user does not know much about classical music, how should they even begin to find a piece that they might like. Similarly, for patients or their carers, if they don't know the right keywords for their health problems, how can they effectively find useful health information for themselves? [11]

Designing new interfaces

With one of the motivations being to support users when keyword search is not enough, some research has focused on identifying alternative user interfaces and interaction models that support the user in different ways. An example is faceted search which presents diverse category-style options to the users, so that they can choose from a list instead of guess a possible keyword query.

Many of the interactive forms of search, including faceted browsers, are being considered for their support of exploratory search conditions.

Computational cognitive models of exploratory search have been developed to capture the cognitive complexities involved in exploratory search. Model-based dynamic presentation of information cues are proposed to facilitate exploratory search performance. [12]

Evaluating interfaces

As the tasks and goals involved with exploratory search are largely undefined or unpredictable, it is very hard to evaluate systems with the measures often used in information retrieval. Accuracy was typically used to show that a user had found a correct answer, but when the user is trying to summarize a domain of information, the correct answer is near impossible to identify, if not entirely subjective (for example: possible hotels to stay in Paris). In exploration, it is also arguable that spending more time (where time efficiency is typically desirable) researching a topic shows that a system provides increased support for investigation. Finally, and perhaps most importantly, giving study participants a well specified task could immediately prevent them from exhibiting exploratory behavior.[ citation needed ]

Models of exploratory search behavior

There have been recent[ when? ] attempts to develop a process model of exploratory search behavior, especially in social information system (e.g., see models of collaborative tagging. [13] [14] The process model assumes that user-generated information cues, such as social tags, can act as navigational cues that facilitate exploration of information that others have found and shared with other users on a social information system (such as social bookmarking system). These models provided extension to existing process model of information search that characterizes information-seeking behavior in traditional fact-retrievals using search engines. [15] [16] [17] Recent[ when? ] development in exploratory search is often concentrated in predicting users' search intents in interaction with the user. [18] Such predictive user modeling, also referred as intent modeling, can help users to get accustomed to a body of domain knowledge and help users to make sense of the potential directions to be explored around their initial, often vague, expression of information needs. [19] [20] [21]

Major figures

Key figures, including experts from both information seeking and human–computer interaction, are:[ according to whom? ]

Related Research Articles

Personal information management (PIM) is the study and implementation of the activities that people perform in order to acquire or create, store, organize, maintain, retrieve, and use informational items such as documents, web pages, and email messages for everyday use to complete tasks and fulfill a person's various roles ; it is information management with intrapersonal scope. Personal knowledge management is by some definitions a subdomain.

An image retrieval system is a computer system used for browsing, searching and retrieving images from a large database of digital images. Most traditional and common methods of image retrieval utilize some method of adding metadata such as captioning, keywords, title or descriptions to the images so that retrieval can be performed over the annotation words. Manual image annotation is time-consuming, laborious and expensive; to address this, there has been a large amount of research done on automatic image annotation. Additionally, the increase in social web applications and the semantic web have inspired the development of several web-based image annotation tools.

<span class="mw-page-title-main">Browsing</span> Exploratory use of an information system

Browsing is a kind of orienting strategy. It is supposed to identify something of relevance for the browsing organism. In context of humans, it is a metaphor taken from the animal kingdom. It is used, for example, about people browsing open shelves in libraries, window shopping, or browsing databases or the Internet.

A recommender system, or a recommendation system, is a subclass of information filtering system that provides suggestions for items that are most pertinent to a particular user. Recommender systems are particularly useful when an individual needs to choose an item from a potentially overwhelming number of items that a service may offer.

<span class="mw-page-title-main">Automatic image annotation</span>

Automatic image annotation is the process by which a computer system automatically assigns metadata in the form of captioning or keywords to a digital image. This application of computer vision techniques is used in image retrieval systems to organize and locate images of interest from a database.

<span class="mw-page-title-main">University of Maryland Human–Computer Interaction Lab</span> Research lab at the University of Maryland, College Park

The Human–Computer Interaction Lab (HCIL) at the University of Maryland, College Park is an academic research center specializing in the field of human-computer interaction (HCI). Founded in 1983 by Ben Shneiderman, it is one of the oldest HCI labs of its kind. The HCIL conducts research on the design, implementation, and evaluation of computer interface technologies. Additional research focuses on the development of user interfaces and design methods. Primary activities of the HCIL include collaborative research, publication and the sponsorship of open houses, workshops and annual symposiums.

Faceted search augments lexical search with a faceted navigation system, allowing users to narrow results by applying filters based on a faceted classification of the items. It is a parametric search technique. A faceted classification system classifies each information element along multiple explicit dimensions, facets, enabling the classifications to be accessed and ordered in multiple ways rather than in a single, predetermined, taxonomic order.

Human–computer information retrieval (HCIR) is the study and engineering of information retrieval techniques that bring human intelligence into the search process. It combines the fields of human-computer interaction (HCI) and information retrieval (IR) and creates systems that improve search by taking into account the human context, or through a multi-step search process that provides the opportunity for human feedback.

In computing, 3D interaction is a form of human-machine interaction where users are able to move and perform interaction in 3D space. Both human and machine process information where the physical position of elements in the 3D space is relevant.

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.

Collaborative tagging, also known as social tagging or folksonomy, allows users to apply public tags to online items, typically to make those items easier for themselves or others to find later. It has been argued that these tagging systems can provide navigational cues or "way-finders" for other users to explore information. The notion is that given that social tags are labels users create to represent topics extracted from online documents, the interpretation of these tags should allow other users to predict the contents of different documents efficiently. Social tags are arguably more important in exploratory search, in which the users may engage in iterative cycles of goal refinement and exploration of new information, and interpretation of information contents by others will provide useful cues for people to discover topics that are relevant.

Collaborative information seeking (CIS) is a field of research that involves studying situations, motivations, and methods for people working in collaborative groups for information seeking projects, as well as building systems for supporting such activities. Such projects often involve information searching or information retrieval (IR), information gathering, and information sharing. Beyond that, CIS can extend to collaborative information synthesis and collaborative sense-making.

<span class="mw-page-title-main">International Society for Music Information Retrieval</span>

The International Society for Music Information Retrieval (ISMIR) is an international forum for research on the organization of music-related data. It started as an informal group steered by an ad hoc committee in 2000 which established a yearly symposium - whence "ISMIR", which meant International Symposium on Music Information Retrieval. It was turned into a conference in 2002 while retaining the acronym. ISMIR was incorporated in Canada on July 4, 2008.

Social navigation is a form of social computing introduced by Paul Dourish and Matthew Chalmers in 1994, who defined it as when "movement from one item to another is provoked as an artifact of the activity of another or a group of others". According to later research in 2002, "social navigation exploits the knowledge and experience of peer users of information resources" to guide users in the information space, and that it is becoming more difficult to navigate and search efficiently with all the digital information available from the World Wide Web and other sources. Studying others' navigational trails and understanding their behavior can help improve one's own search strategy by guiding them to make more informed decisions based on the actions of others.

Video browsing, also known as exploratory video search, is the interactive process of skimming through video content in order to satisfy some information need or to interactively check if the video content is relevant. While originally proposed to help users inspecting a single video through visual thumbnails, modern video browsing tools enable users to quickly find desired information in a video archive by iterative human–computer interaction through an exploratory search approach. Many of these tools presume a smart user that wants features to interactively inspect video content, as well as automatic content filtering features. For that purpose, several video interaction features are usually provided, such as sophisticated navigation in video or search by a content-based query. Video browsing tools often build on lower-level video content analysis, such as shot transition detection, keyframe extraction, semantic concept detection, and create a structured content overview of the video file or video archive. Furthermore, they usually provide sophisticated navigation features, such as advanced timelines, visual seeker bars or a list of selected thumbnails, as well as means for content querying. Examples of content queries are shot filtering through visual concepts, through some specific characteristics, through user-provided sketches, or through content-based similarity search.

Gary Marchionini is an American information scientist and educator at the University of North Carolina at Chapel Hill (1998–present).

Animal–computer interaction (ACI) is a field of research for the design and use of technology with, for and by animals covering different kinds of animals from wildlife, zoo and domesticated animals in different roles. It emerged from, and was heavily influenced by, the discipline of Human–computer interaction (HCI). As the field expanded, it has become increasingly multi-disciplinary, incorporating techniques and research from disciplines such as artificial intelligence (AI), requirements engineering (RE), and veterinary science.

Andrew Cockburn is currently working as a Professor in the Department of Computer Science and Software Engineering at the University of Canterbury in Christchurch, New Zealand. He is in charge of the Human Computer Interactions Lab where he conducts research focused on designing and testing user interfaces that integrate with inherent human factors.

<span class="mw-page-title-main">Shumin Zhai</span> Human–computer interaction research scientist

Shumin Zhai is a Chinese-born American Canadian Human–computer interaction (HCI) research scientist and inventor. He is known for his research specifically on input devices and interaction methods, swipe-gesture-based touchscreen keyboards, eye-tracking interfaces, and models of human performance in human-computer interaction. His studies have contributed to both foundational models and understandings of HCI and practical user interface designs and flagship products. He previously worked at IBM where he invented the ShapeWriter text entry method for smartphones, which is a predecessor to the modern Swype keyboard. Dr. Zhai's publications have won the ACM UIST Lasting Impact Award and the IEEE Computer Society Best Paper Award, among others, and he is most known for his research specifically on input devices and interaction methods, swipe-gesture-based touchscreen keyboards, eye-tracking interfaces, and models of human performance in human-computer interaction. Dr. Zhai is currently a Principal Scientist at Google where he leads and directs research, design, and development of human-device input methods and haptics systems.

<span class="mw-page-title-main">Hanna Wallach</span> Computational social scientist

Hanna Wallach is a computational social scientist and partner research manager at Microsoft Research. Her work makes use of machine learning models to study the dynamics of social processes. Her current research focuses on issues of fairness, accountability, transparency, and ethics as they relate to AI and machine learning.

References

  1. ↑ Ryen W. White and Resa A. Roth (2009). Exploratory Search: Beyond the Query-Response Paradigm, San Rafael, CA: Morgan and Claypool.
  2. 1 2 "HCIL SOH 2005 Workshop on Exploratory Search Interfaces". Microsoft. Retrieved 8 April 2016.
  3. ↑ "SIGIR 2006 Workshop - Evaluating Exploratory Search Systems". Microsoft. Retrieved 8 April 2016.
  4. ↑ "Sigir 2006" . Retrieved 8 April 2016.
  5. ↑ "CHI 2007 Workshop - Exploratory Search and HCI". Microsoft. Retrieved 8 April 2016.
  6. ↑ "CHI 2007 Reach Beyond - welcome" . Retrieved 8 April 2016.
  7. ↑ "Information Processing & Management" . Retrieved 8 April 2016.
  8. ↑ Ryen W. White, Gary Marchionini, Gheorghe Muresan (2008). Evaluating exploratory search systems: Introduction to special topic issue of information processing and management Vol. 44, Issue 2, (2008), pp. 433–436
  9. ↑ "Moved" . Retrieved 8 April 2016.
  10. ↑ mSpace
  11. ↑ Pang, PCI; Verspoor, K; Chang, S; Pearce, J (2015). "Conceptualising health information seeking behaviours and exploratory search: result of a qualitative study". Health and Technology. 5 (1): 45–55. doi:10.1007/s12553-015-0096-0. S2CID   54067666.
  12. ↑ Fu, W.-T., Kannampalill, T. G., & Kang, R. (2010). Facilitating exploratory search by model-based navigational cues. In Proceedings of the ACM International conference on Intelligent User Interface. 199–208.
  13. ↑ Fu, Wai-Tat (April 2008), "The microstructures of social tagging", Proceedings of the 2008 ACM conference on Computer supported cooperative work, pp. 66–72, doi:10.1145/1460563.1460600, ISBN   978-1-60558-007-4, S2CID   2202814
  14. ↑ Fu, Wai-Tat (Aug 2009), "A Semantic Imitation Model of Social Tagging" (PDF), Proceedings of the IEEE Conference on Social Computing: 66–72, archived from the original (PDF) on 2009-12-29
  15. ↑ Fu, Wai-Tat; Pirolli, Peter (2007), "SNIF-ACT: a cognitive model of user navigation on the world wide web", Human-Computer Interaction, 22: 335–412
  16. ↑ Kitajima, M., Blackmon, M. H., & Polson, P. G. (2000). A comprehension-based model of Web navigation and its application to Web usability analysis. In S. Mc-Donald, Y. Waern, & G. Cockton (Eds.), People and computers XIV—Usability or else! New York: Springer-Verlag.
  17. ↑ Miller, C. S., & Remington, R.W. (2004). Modeling information navigation: Implications for information architecture. Human Computer Interaction, 19, 225–271.
  18. ↑ Ruotsalo, Tuukka; Athukorala, Kumaripaba; Glowacka, Dorota; Konuyshkova, Ksenia; Oulasvrita, Antti; Kaipiainen, Samuli; Kaski, Samuel; Jacucci, Giulio (2013), "Supporting exploratory search tasks with interactive user modeling", Proceedings of the 76th Annual Meeting of the American Society for Information Science and Technology ASIS&T
  19. ↑ Ruotsalo, Tuukka; Peltonen, Jaakko; Eugster, Manuel J.A.; Glowacka, Dorota; Floréen, Patrik; Myllymäki, Petri; Jacucci, Giulio; Kaski, Samuel (2018), "Interactive Intent Modeling for Exploratory Search", ACM Transactions on Information Systems, 36 (4): 1–46, doi: 10.1145/3231593
  20. ↑ Ruotsalo, Tuukka; Peltonen, Jaakko; Eugster, Manuel J.A.; Glowacka, Dorota; Konuyshkova, Ksenia; Athukorala, Kumaripaba; Kosunen, Ilkka; Reijonen, Aki; Myllymäki, Petri; Kaski, Samuel; Jacucci, Giulio (2013), "Directing exploratory search with interactive intent modeling", Proceedings of the 22nd ACM international conference on Conference on information & knowledge management - CIKM '13, pp. 1759–1764, doi:10.1145/2505515.2505644, ISBN   9781450322638, S2CID   875392
  21. ↑ Glowacka, Dorota; Ruotsalo, Tuukka; Konuyshkova, Ksenia; Athukorala, Kumaripaba; Kaski, Samuel; Jacucci, Giulio (2013), "Directing exploratory search", Proceedings of the 2013 international conference on Intelligent user interfaces, IUI '13, pp. 117–128, doi:10.1145/2449396.2449413, ISBN   9781450319652, S2CID   14354487
  22. ↑ "Nick's home page" . Retrieved 17 April 2016.
  23. ↑ "Gary's Home Page" . Retrieved 8 April 2016.
  24. ↑ "m.c. schraefel: design for innovation, creativity, discovery" . Retrieved 8 April 2016.
  25. ↑ "Ryen W. White". Microsoft. Retrieved 8 April 2016.

Sources

  1. White, R.W., Kules, B., Drucker, S.M., and schraefel, m.c. (2006). Supporting Exploratory Search, Introduction to Special Section of Communications of the ACM, Vol. 49, Issue 4, (2006), pp. 36–39.
  2. Ryen W. White, Gary Marchionini, Gheorghe Muresan (2008). Evaluating exploratory search systems: Introduction to special topic issue of information processing and management Vol. 44, Issue 2, (2008), pp. 433–436
  3. Ryen W. White and Resa A. Roth (2009). Exploratory Search: Beyond the Query-Response Paradigm, San Rafael, CA: Morgan and Claypool.
  4. P. Papadakos, S. Kopidaki, N. Armenatzoglou and Y. Tzitzikas (2009). Exploratory Web Searching with Dynamic Taxonomies and Results Clustering,13th European Conference on Digital Libraries (ECDL'09), Corfu, Greece, Sep-Oct 2009
  5. Marchionini, G. (2006). Exploratory Search: From Finding to Understanding, Communications of the ACM, 49(2), p. 41-46.
This page is based on this Wikipedia article
Text is available under the CC BY-SA 4.0 license; additional terms may apply.
Images, videos and audio are available under their respective licenses.