Exploratory search

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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

Information retrieval (IR) is the activity of obtaining information system resources that are relevant to an information need from a collection of those resources. Searches can be based on full-text or other content-based indexing. Information retrieval is the science of searching for information in a document, searching for documents themselves, and also searching for the metadata that describes data, and for databases of texts, images or sounds.

In information science and information retrieval, relevance denotes how well a retrieved document or set of documents meets the information need of the user. Relevance may include concerns such as timeliness, authority or novelty of the result.

A recommender system, or a recommendation system, is a subclass of information filtering system that seeks to predict the "rating" or "preference" a user would give to an item. They are primarily used in commercial applications.

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.

The Gerard Salton Award is presented by the Association for Computing Machinery (ACM) Special Interest Group on Information Retrieval (SIGIR) every three years to an individual who has made "significant, sustained and continuing contributions to research in information retrieval". SIGIR also co-sponsors the Vannevar Bush Award, for the best paper at the Joint Conference on Digital Libraries.

Relevance feedback is a feature of some information retrieval systems. The idea behind relevance feedback is to take the results that are initially returned from a given query, to gather user feedback, and to use information about whether or not those results are relevant to perform a new query. We can usefully distinguish between three types of feedback: explicit feedback, implicit feedback, and blind or "pseudo" feedback.

Query expansion (QE) is the process of reformulating a given query to improve retrieval performance in information retrieval operations, particularly in the context of query understanding. In the context of search engines, query expansion involves evaluating a user's input and expanding the search query to match additional documents. Query expansion involves techniques such as:

A web search query is a query based on a specific search term that a user enters into a web search engine to satisfy his or her information needs. Web search queries are distinctive in that they are often plain text or hypertext with optional search-directives. They vary greatly from standard query languages, which are governed by strict syntax rules as command languages with keyword or positional parameters.

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.

A concept search is an automated information retrieval method that is used to search electronically stored unstructured text for information that is conceptually similar to the information provided in a search query. In other words, the ideas expressed in the information retrieved in response to a concept search query are relevant to the ideas contained in the text of the query.

XML retrieval, or XML information retrieval, is the content-based retrieval of documents structured with XML. As such it is used for computing relevance of XML documents.

An intelligent medical search engine is a vertical search engine that uses expert system technology to provide personalized medical information.

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.

Learning to rank

Learning to rank or machine-learned ranking (MLR) is the application of machine learning, typically supervised, semi-supervised or reinforcement learning, in the construction of ranking models for information retrieval systems. Training data consists of lists of items with some partial order specified between items in each list. This order is typically induced by giving a numerical or ordinal score or a binary judgment for each item. The ranking model purposes to rank, i.e. producing a permutation of items in new, unseen lists in a similar way to rankings in the training data.

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.

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.

User intent, otherwise known as query intent or search intent, is the identification and categorisation of what a user online intended or wanted to find when they typed their search terms into an online web search engine for the purpose of search engine optimisation or conversion rate optimisation. Examples of user intent are fact-checking, comparison shopping or filling downtime.

Query understanding is the process of inferring the intent of a search engine user by extracting semantic meaning from the searcher’s keywords. Query understanding methods generally take place before the search engine retrieves and ranks results. It is related to natural language processing but specifically focused on the understanding of search queries. Query understanding is at the heart of technologies like Amazon Alexa, Apple's Siri. Google Assistant, IBM's Watson, and Microsoft's Cortana.

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

Andrew (Andy) 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.

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.
  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: A Rational Model", Proceedings of the ACM 2008 Conference on Computer Supported Cooperative Work.: 66–72, doi:10.1145/1460563.1460600, ISBN   978-1-60558-007-4
  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 ACM Conference of Information and Knowledge Management CIKM: 1759–1764, doi:10.1145/2505515.2505644, ISBN   9781450322638
  21. ↑ Glowacka, Dorota; Ruotsalo, Tuukka; Konuyshkova, Ksenia; Athukorala, Kumaripaba; Kaski, Samuel; Jacucci, Giulio (2013), "Directing exploratory search: Reinforcement learning from user interactions with keywords", Proceedings of the ACM Conference of Intelligent User Interfaces IUI: 117–128
  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.
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