Collaborative Information Seeking

Golovchinsky, Gene; Qvarfordt, Pernilla; Pickens, Jeremy

doi:10.1109/mc.2009.73

Cited by 94 publications

(88 citation statements)

References 5 publications

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“…In this part, our objective is speci cally to propose a synthesis of the di erent forms of collaboration in IR, whether based on user-to-system [132] or user-to-user interactions [30,87,96]. Then, considering the particular scenario of CIR de ned in References [27,30], we will introduce a general framework in terms of session modeling.…”

Section: Collaboration and Information Retrievalmentioning

confidence: 99%

“…Then, considering the particular scenario of CIR de ned in References [27,30], we will introduce a general framework in terms of session modeling.…”

Section: Collaboration and Information Retrievalmentioning

confidence: 99%

“…Other dimensions (e.g., the mediation level, the concurrency, and the location) distinguish these collaboration-based IR domains [12,30,105], as synthesized in Table 1. More particularly, We identify (a) the user-oriented mediation in which the entire achievement of the search session relies on the user (for instance, through search interfaces) and (b) the system-based mediation in which the collaboration is supported by algorithmic approaches.…”

Section: The DI Erent Forms Of Collaborationmentioning

confidence: 99%

“…• Collaborative ltering [76,96], in which the relevance depends on preferences of users with same interests; • Social IR [3,87], which takes into account users' interactions through the social network and social evidence sources, such as the users' betweenness or authority; • Collaborative IR (CIR) [27,30], in which several users work together with the goal of solving a complex information need.…”

Section: Introductionmentioning

confidence: 99%

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On the Collaboration Support in Information Retrieval

Soulier

Tamine

2017

ACM Comput. Surv.

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Collaborative Information Retrieval (CIR) is a well-known setting in which explicit collaboration occurs among a group of users working together to solve a shared information need. This type of collaboration has been deemed as bene cial for complex or exploratory search tasks. With the multiplicity of factors impacting on the search e ectiveness (e.g., collaborators' interactions or the individual perception of the shared information need), CIR gives rise to several challenges in terms of collaboration support through algorithmic approaches. More particularly, CIR should allow us to satisfy the shared information need by optimizing the collaboration within the search session over all collaborators, while ensuring that both mutually bene cial goals are reached and that the cognitive cost of the collaboration does not impact the search e ectiveness. In this survey, we propose an overview of CIR with a particular focus on the collaboration support through algorithmic approaches. The objective of this article is (a) to organize previous empirical studies analyzing collaborative search with the goal to provide useful design implications for CIR models, (b) to give a picture of the CIR area by distinguishing two main categories of models using the collaboration mediation axis, and (c) to point out potential perspectives in the domain.

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Section: Collaboration and Information Retrievalmentioning

confidence: 99%

“…Then, considering the particular scenario of CIR de ned in References [27,30], we will introduce a general framework in terms of session modeling.…”

Section: Collaboration and Information Retrievalmentioning

confidence: 99%

Section: The DI Erent Forms Of Collaborationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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On the Collaboration Support in Information Retrieval

Soulier

Tamine

2017

ACM Comput. Surv.

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“…In short, many researchers have begun to consider the role of collaboration during information search and content discovery; see for example the work of Ariadne [10], SearchTogether [6] and CoSearch [1]. Golovchinsky et al [2] proposes a taxonomy of collaborative information sharing highlight key dimensions such as the intent, depth, concurrency, and location for a variety of collaborative information services. Very briefly, for example, Golovchinsky et al distinguish between services that support implicit versus explicit collaboration, services that over shallow UIbased collaboration versus deeper algorithmic support for collaboration, services that support synchronous versus asynchronous collaboration , and finally those services that assume information seekers are co-located versus those that assume remote collaboration.…”

Section: Introductionmentioning

confidence: 99%

Recognising and Recommending Context in Social Web Search

Saaya¹,

Smyth²,

Coyle³

et al. 2011

User Modeling, Adaption and Personalization

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PublisherSpringer Abstract. In this paper we focus on an approach to social search, HeyStaks that is designed to integrate with mainstream search engines such as Google, Yahoo and Bing. HeyStaks is motivated by the idea that Web search is an inherently social or collaborative activity. Heystaks users search as normal but benefit from collaboration features, allowing searchers to better organise and share their search experiences. Users can create and share repositories of search knowledge (so-called search staks) in order to benefit from the searches of friends and colleagues. As such search staks are community-based information resources. A key challenge for HeyStaks is predicting which search stak is most relevant to the users current search context and in this paper we focus on this so-called stak recommendation issue by looking at a number of different approaches to profling and recommending community-search knowledge. Link to online version

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Toward a model of collaborative information behavior in organizations

Karunakaran

Reddy

Spence

2013

J Am Soc Inf Sci Tec

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There is increasing interest in topics at the nexus of collaboration and information behavior. A variety of studies conducted in organizational settings have provided us with key insights about the collaborative aspects of seeking, retrieving, and using information. Researchers have used a range of terms, including collaborative information seeking (CIS), collaborative information retrieval (CIR), collaborative search, collaborative sensemaking, and others to describe various pertinent activities. Consequently, we lack conceptual clarity concerning these activities, leading to a tendency to use terms interchangeably when in fact they may be referring to different issues. Here, we offer collaborative information behavior (CIB) as an umbrella term to connote the collaborative aspects of information seeking, retrieval, and use. We provide the contours of a model of CIB synthesized from findings of past studies conducted by our research team as well as other researchers. By reanalyzing and synthesizing the data from those studies, we conceptualize CIB as comprised of a set of constitutive activities, organized into three broad phases—problem formulation, collaborative information seeking, and information use. Some of the activities are specific to a particular phase, whereas others are common to all phases. We explain how those constitutive activities are related to one another. Finally, we discuss the limitations of our model as well as its potential usefulness in advancing CIB research.

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Collaborative Information Seeking

Cited by 94 publications

References 5 publications

On the Collaboration Support in Information Retrieval

On the Collaboration Support in Information Retrieval

Recognising and Recommending Context in Social Web Search

Toward a model of collaborative information behavior in organizations

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