Evaluating the synergic effect of collaboration in information seeking

Shah, Chirag; González‐Ibáñez, Roberto

doi:10.1145/2009916.2010038

Cited by 78 publications

(116 citation statements)

References 18 publications

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“…Orthogonally to interactive and dynamic IR, CIR also integrates the user-system collaboration for both ensuring the document relevance at the query level [25,89] and optimizing the synergic e ect of the search session [109].…”

Section: The DI Erent Forms Of Collaborationmentioning

confidence: 99%

“…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. In the context of CIR, the mediation at the user level is generally supported by search interfaces, such as SearchTogether [79] or Coagmento [109], while the system-level restricts users' actions by providing rankings [25] or query suggestions [89]. The consideration of the user-driven mediation is a particular setting in CIR in which the system lets the user performing his/her task and applies dynamically the best suited adapted retrieval techniques (e.g., automatically expanding the query with speci c words or suggesting query reformulations) with respect to his/her behavior.…”

Section: The DI Erent Forms Of Collaborationmentioning

confidence: 99%

“…Empirical studies [55,109,137] are a well-known framework in IR for understanding users' behavior, exploring the impact of features, and testing the e ectiveness of ranking models [21]. In our particular context of explicit collaborative search, those studies have been used, for instance, to identify (a) the value of collaboration with respect to individual scenarios, (b) the manifestation of the collaboration in search tasks, and (c) the impact of di erent collaborative settings on the overall e ectiveness of the search session.…”

Section: Empirical Understanding Of Collaborative Searchmentioning

confidence: 99%

“…However, fundamental di erences exist between individual and collaborative search sessions [9,62,64,109], according to the diversity of the vocabulary, the interaction mode, and the search e ectiveness. Moreover, a collaborative search setting involves additional di culties due to the important place of communication between collaborators, the complexity of the shared information needs, and the cognitive load of the sense-making process.…”

Section: Empirical Understanding Of Collaborative Searchmentioning

confidence: 99%

“…This second category of empirical studies highlights the impact of the spatial [33,109] and temporal [32,43] factors on the collaborative search e ectiveness.…”

Section: Studying the Impact Of Time And Space On Collaborative Searchmentioning

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: The DI Erent Forms Of Collaborationmentioning

confidence: 99%

Section: The DI Erent Forms Of Collaborationmentioning

confidence: 99%

Section: Empirical Understanding Of Collaborative Searchmentioning

confidence: 99%

Section: Empirical Understanding Of Collaborative Searchmentioning

confidence: 99%

“…This second category of empirical studies highlights the impact of the spatial [33,109] and temporal [32,43] factors on the collaborative search e ectiveness.…”

Section: Studying the Impact Of Time And Space On Collaborative Searchmentioning

confidence: 99%

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On the Collaboration Support in Information Retrieval

Soulier

Tamine

2017

ACM Comput. Surv.

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Capturing Collabportunities: A method to evaluate collaboration opportunities in information search using pseudocollaboration

González‐Ibáñez

Shah

White

2014

Asso for Info Science & Tech

Self Cite

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In explicit collaborative search, two or more individuals coordinate their efforts toward a shared goal. Every day, Internet users with similar information needs have the potential to collaborate. However, online search is typically performed in solitude. Existing search systems do not promote explicit collaborations, and collaboration opportunities (collabportunities) are missed. In this article, we describe a method to evaluate the feasibility of transforming these collabportunities into recommendations for explicit collaboration. We developed a technique called pseudocollaboration to evaluate the benefits and costs of collabportunities through simulations. We evaluate the performance of our method using three data sets: (a) data from single users' search ses- sions, (b) data with collaborative search sessions between pairs of searchers, and (c) logs from a largescale search engine with search sessions of thousands of searchers. Our results establish when and how collabportunities would significantly help or hinder the search process versus searches conducted individually.The method that we describe has implications for the design and implementation of recommendation systems for explicit collaboration. It also connects systemmediated and user-mediated collaborative search, whereby the system evaluates the likely benefits of collaborating for a search task and helps searchers make more informed decisions on initiating and executing such a collaboration.

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Rain or shine? Forecasting search process performance in exploratory search tasks

Shah

Hendahewa

González‐Ibáñez

2015

Asso for Info Science & Tech

Self Cite

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Most information retrieval (IR) systems consider relevance, usefulness, and quality of information objects (documents, queries) for evaluation, prediction, and recommendation, often ignoring the underlying search process of information seeking. This may leave out opportunities for making recommendations that analyze the search process and/or recommend alternative search process instead of objects. To overcome this limitation, we investigated whether by analyzing a searcher's current processes we could forecast his likelihood of achieving a certain level of success with respect to search performance in the future. We propose a machine-learning-based method to dynamically evaluate and predict search performance several time-steps ahead at each given time point of the search process during an exploratory search task. Our prediction method uses a collection of features extracted from expression of information need and coverage of information. For testing, we used log data collected from 4 user studies that included 216 users (96 individuals and 60 pairs). Our results show 80-90% accuracy in prediction depending on the number of time-steps ahead. In effect, the work reported here provides a framework for evaluating search processes during exploratory search tasks and predicting search performance. Importantly, the proposed approach is based on user processes and is independent of any IR system. IntroductionPredicting how people perform in their information search processes is a hard problem. The prediction problem becomes even more complex when considering exploratory searches. Exploratory search is typically described as openended and multifaceted, where the goals may be unclear and there may be no or multiple satisfactory answers ( Marchionini, 2006;Wildemuth & Freund, 2012). Searchers engage in exploratory search when they commence researching a new topic, when they form a new idea, in problem identification, and other forms of information seeking for creative discovery. The searcher shifts out of an exploratory search when they have defined the issues and problems in the new topic area and are now sure of their information need. MacKay (1969) and Taylor (1968) explain this difference in terms of issuing a command to the information system and asking a question of the information system. A command from the user wishes to affect the "goalsetting" levers in the information system, while a question from the user wishes the system to affect the goal-settings, the range of state of readiness, in the user (MacKay, 1969, p. 101; see also, Cole, 2012, p. 20).Although modern information retrieval (IR) systems aim to support exploratory search, such systems are often unable to perform dynamic and timely predictions of their users' search performance. Exploratory search as a combination of browsing and focused search involves a variety of key factors such as context, intentions, motivations, prior knowledge, feelings, expectations, and strategies, which are often ignored by these IR systems. In the absence of such informati...

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Evaluating the synergic effect of collaboration in information seeking

Cited by 78 publications

References 18 publications

On the Collaboration Support in Information Retrieval

On the Collaboration Support in Information Retrieval

Capturing Collabportunities: A method to evaluate collaboration opportunities in information search using pseudocollaboration

Rain or shine? Forecasting search process performance in exploratory search tasks

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