Capacitated team formation problem on social networks

Majumder, Anirban; Datta, Samik; Naidu, K. V. M.

doi:10.1145/2339530.2339690

Cited by 114 publications

(44 citation statements)

References 13 publications

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“…Inspired by Majumder et al [15], it is easy to get the approximation ratio of Algorithm 2. Due to the limited space, the details of the approximation ratio proof are omitted in this paper.…”

Section: Top-1 Approximation Algorithmmentioning

confidence: 99%

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Top-k Team Recommendation and Its Variants in Spatial Crowdsourcing

et al. 2017

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With the rapid development of mobile internet and online to offline marketing model, various spatial crowdsourcing platforms, such as Gigwalk and Gmission, are getting popular. Most existing studies assume that spatial crowdsourced tasks are simple and trivial. However, many real crowdsourced tasks are complex and need to be collaboratively finished by a team of crowd workers with different skills. Therefore, an important issue of spatial crowdsourcing platforms is to recommend some suitable teams of crowd workers to satisfy the requirements of skills in a task. In this paper, to address the issue, we first propose a more practical problem, called Top-k team recommendation in spatial crowdsourcing (TopkTR) problem. We prove that the TopkTR problem is NP-hard and designs a two-level-based framework, which includes an approximation algorithm with provable approximation ratio and an exact algorithm with pruning techniques to address it. In addition, we study a variant of the TopkTR problem, called TopkTRL, where a team leader is appointed among each recommended team of crowd workers in order to coordinate different crowd workers conveniently, and the aforementioned framework can be extended to address this variant. Finally, we verify the effectiveness and efficiency of the proposed methods through extensive experiments on real and synthetic datasets.

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Section: Top-1 Approximation Algorithmmentioning

confidence: 99%

“…Anagnostopoulos et al [26,27] further studies the workload balance issue in the static and dynamic team formation problem. The capacity constraint of experts is also considered as an variant of the team formation problem in [15]. Moreover, the problems of discovering crowd experts in social media market are also studied [28,29].…”

Section: Team Formation Problemmentioning

confidence: 99%

Top-k Team Recommendation and Its Variants in Spatial Crowdsourcing

et al. 2017

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“…Recently, Anagnostopoulos et al [1] extended the work of Lappas et al [10] where tasks arrive in an online fashion and it is required to balance the work load across team members. In a related paper, Majumder et al [12] defined a notion of capacity for each expert and put an additional constraint of capacity violation.…”

Section: Prior Artmentioning

confidence: 99%

“…To summarize, the initial work of Lappas et al [10] and all its subsequent extensions [8,9,11,6,1,12] formulated the team formation problem as some kind of a combinatorial optimization problem and gave approximation algorithms for the same, as the original problems turned out to be NP-hard. For all these formulations, skill cover is a default constraint which ensures that a required set of skills are available in the selected team.…”

Section: Prior Artmentioning

confidence: 99%

Submodularity in Team Formation Problem

Bhowmik

Borkar

Garg³

et al. 2014

Proceedings of the 2014 SIAM International Conference on Data Mining

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We consider the team formation problem where the goal is to find a team of experts for a specific project. In the past, several attempts have been made to formulate this problem and each formulation focuses only on a subset of design criteria such as skill coverage, social compatibility, economy, skill redundancy, etc. In this paper, for the first time, we show that most of the important design criteria for this problem can be fully modeled within one single formulation as an unconstrained submodular function maximization problem. In our formulation, the submodular function turns out to be non-negative and non-monotone. The maximization of this class of submodular function is much less explored than its monotone constrained counterpart. A few recent works [7] [4] have come up with a simulated annealing based randomized approximation scheme for this problem with an approximation ratio of 0.41. In this paper, we customize this algorithm to our formulation and conduct an extensive set of experiments to show its efficacy. Our proposed formulation offers several advantageous features over the existing formulations including skill cover softening, better team communication, and connectivity relaxation. Unlike previous formulations, the skill cover softening feature allows a designer to specify Must Have and Should Have skills. Similarly, through better team communication, we avoid the restriction that all the communication among team members should pass through only the team members and not the outsiders. Finally, connectivity relaxation feature alleviates the constraint of whole team being connected and thereby, lowering the cost.

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“…These approaches differ among themselves in their models of measuring and handling expertise and in the ways in which they find the solution. Another line of approaches [20,22] is to form teams containing members that cover a prespecified set of skills while minimizing the communication costs indicated by the social network of the team members. Both models have in common that the resulting teams tend to be heterogeneous.…”

Section: Introductionmentioning

confidence: 99%

Using Patterns to Form Homogeneous Teams

et al. 2013

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Homogeneous team formation is the task of grouping individuals into teams, each of which consists of members who fulfill the same set of prespecified properties. In this theoretical work, we propose, motivate, and analyze a combinatorial model where, given a matrix over a finite alphabet whose rows correspond to individuals and columns correspond to attributes of individuals, the user specifies lower and upper bounds on team sizes as well as combinations of attributes that have to be homogeneous (that is, identical) for all members of the corresponding teams. Furthermore, the user can define a cost for assigning any individual to a certain team. We show that some special cases of our new model lead to NP-hard problems while others allow for (fixed-parameter) tractability results. For example, the problem is already That version concentrates on the anonymization aspects of the model. In our new version we slightly extend our model and show how it applies to (homogeneous) clustering of individuals, that is, to homogeneous team formation. Indeed, we now claim that the models and ideas better fit with these applications than with the previous data anonymization motivation. Apart from full proofs omitted in the extended abstract and also adapting our old ideas to the new extended model, the current article also contains a new and easier proof of NP-hardness, a new proof for showing that polynomial-time data reduction in term of so-called polynomial-size problem kernels is unlikely to exist with respect to certain parameterizations, and a new algorithm for the (still NP-hard) special case ignoring costs. Many of the new findings are part of the diploma thesis [18] NP-hard even if (i) there are no lower and upper bounds on the team sizes, (ii) all costs are zero, and (iii) the matrix has only two columns. In contrast, the problem becomes fixed-parameter tractable for the combined parameter "number of possible teams" and "number of different individuals", the latter being upper-bounded by the number of rows.

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Capacitated team formation problem on social networks

Cited by 114 publications

References 13 publications

Top-k Team Recommendation and Its Variants in Spatial Crowdsourcing

Top-k Team Recommendation and Its Variants in Spatial Crowdsourcing

Submodularity in Team Formation Problem

Using Patterns to Form Homogeneous Teams

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