CrowdSTAR: A Social Task Routing Framework for Online Communities

Nushi, Besmira; Alonso, Omar; Hentschel, Martin; Kandylas, Vasileios

doi:10.1007/978-3-319-19890-3_15

Cited by 5 publications

(4 citation statements)

References 23 publications

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“…Indeed, in addition to bene t from the crowd, a socialoriented CIR model would enable to connect larger groups, eventually on social platforms, and let them work together to leverage from a wider range of complementarity between collaborators. In this context, some approaches have been proposed [85,121]. However, further work addressing three main tasks remains:…”

Section: Conclusion and Promising Perspectivesmentioning

confidence: 99%

“…For instance, in References [4,112,124], the authors investigate the value of a collaborative activity in social networks or question-answering platforms to achieve a search task. One interesting challenge in this eld is to build the collaborative group according to users' compatibility, availability, and expertise [14,85,94,121]. Regardless of the task goal, another emerging line of work focuses on collaborative task optimization in crowd-sourcing platforms [1,71,93].…”

Section: Conclusion and Promising Perspectivesmentioning

confidence: 99%

See 1 more Smart Citation

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: Conclusion and Promising Perspectivesmentioning

confidence: 99%

Section: Conclusion and Promising Perspectivesmentioning

confidence: 99%

On the Collaboration Support in Information Retrieval

Soulier

Tamine

2017

ACM Comput. Surv.

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show abstract

“…But, from the collaboration point of view, it leads to interesting perspectives since it would allow favoring interactions between the information provider and the recommended users. The most intuitive frameworks in which collaboration occurs are community question-answering [10] or social networks [7]. In this context, two approaches are emerging: (1) recommending users to mention in order to favor implicit collaboration [5], and (2) recommending users willing to answer based on explicit collaboration [6].…”

Section: Recommending Usersmentioning

confidence: 99%

Collaborative Information Retrieval

Tamine

Soulier

2016

Proceedings of the 2016 ACM International Conference on the Theory of Information Retrieval

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“…(Venanzi et al 2014) instead, refers to groups as communities and all of them are used for aggregation but not for optimization. Other systems like CrowdSearcher by (Brambilla et al 2014) and CrowdSTAR by (Nushi et al 2015) support cross-community task allocation.…”

Section: Related Workmentioning

confidence: 99%

Crowd Access Path Optimization: Diversity Matters

Nushi

Singla

Gruenheid

et al. 2015

HCOMP

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Quality assurance is one the most important challenges in crowdsourcing. Assigning tasks to several workers to increase quality through redundant answers can be expensive if asking homogeneous sources. This limitation has been overlooked by current crowdsourcing platforms resulting therefore in costly solutions. In order to achieve desirable cost-quality tradeoffs it is essential to apply efficient crowd access optimization techniques. Our work argues that optimization needs to be aware of diversity and correlation of information within groups of individuals so that crowdsourcing redundancy can be adequately planned beforehand. Based on this intuitive idea, we introduce the Access Path Model (APM), a novel crowd model that leverages the notion of access paths as an alternative way of retrieving information. APM aggregates answers ensuring high quality and meaningful confidence. Moreover, we devise a greedy optimization algorithm for this model that finds a provably good approximate plan to access the crowd. We evaluate our approach on three crowdsourced datasets that illustrate various aspects of the problem. Our results show that the Access Path Model combined with greedy optimization is cost-efficient and practical to overcome common difficulties in large-scale crowdsourcing like data sparsity and anonymity.

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CrowdSTAR: A Social Task Routing Framework for Online Communities

Cited by 5 publications

References 23 publications

On the Collaboration Support in Information Retrieval

On the Collaboration Support in Information Retrieval

Collaborative Information Retrieval

Crowd Access Path Optimization: Diversity Matters

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