Profit-maximizing cluster hires

Golshan, Behzad; Lappas, Theodoros; Terzi, Evimaria

doi:10.1145/2623330.2623690

Cited by 28 publications

(11 citation statements)

References 15 publications

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“…Team Formation: In these experiments, we follow the experimental setup of [2,15]. We use a real-world dataset from the online expertise-management platform guru.com that henceforth we refer to as Guru.…”

Section: Methodsmentioning

confidence: 99%

An Efficient Framework for Balancing Submodularity and Cost

Nikolakaki

Ene

Terzi

2021

Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery &Amp; Data Mining

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In the classical selection problem, the input consists of a collection of elements and the goal is to pick a subset of elements from the collection such that some objective function f is maximized. This problem has been studied extensively in the data-mining community and it has multiple applications including influence maximization in social networks, team formation and recommender systems. A particularly popular formulation that captures the needs of many such applications is one where the objective function f is a monotone and non-negative submodular function. In these cases, the corresponding computational problem can be solved using a simple greedy (1 − 1 e )-approximation algorithm. In this paper, we consider a generalization of the above formulation where the goal is to optimize a function that maximizes the submodular function f minus a linear cost function c. This formulation appears as a more natural one, particularly when one needs to strike a balance between the value of the objective function and the cost being paid in order to pick the selected elements. We address variants of this problem both in an offline setting, where the collection is known apriori, as well as in online settings, where the elements of the collection arrive in an online fashion. We demonstrate that by using simple variants of the standard greedy algorithm (used for submodular optimization) we can design algorithms that have provable approximation guarantees, are extremely efficient and work very well in practice. CCS CONCEPTS• Theory of computation → Approximation algorithms analysis.

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Section: Methodsmentioning

confidence: 99%

An Efficient Framework for Balancing Submodularity and Cost

Nikolakaki

Ene

Terzi

2021

Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery &Amp; Data Mining

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“…A key characteristic of our work is that we consider the offline setting where there are multiple tasks, known a-priori, and there is a team formed for each one of them. The offline versions of Anagnostopoulos et al [1,2], the work of Golshan et al [6], as well as the recent work of Barnabó et al [3] consider multiple tasks and multiple teams. However, contrary to our setting, all the above works require that all skills required by the sequence of tasks are completely covered.…”

Section: Multiple Tasks Coveragementioning

confidence: 99%

Finding teams that balance expert load and task coverage

Nikolakaki¹,

Cai²,

Terzi³

2020

Preprint

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The rise of online labor markets (e.g., Freelancer, Guru and Upwork) has ignited a lot of research on team formation, where experts acquiring different skills form teams to complete tasks. The core idea in this line of work has been the strict requirement that the team of experts assigned to complete a given task should contain a superset of the skills required by the task. However, in many applications the required skills are often a wishlist of the entity that posts the task and not all of the skills are absolutely necessary. Thus, in our setting we relax the complete coverage requirement and we allow for tasks to be partially covered by the formed teams, assuming that the quality of task completion is proportional to the fraction of covered skills per task. At the same time, we assume that when multiple tasks need to be performed, the less the load of an expert the better the performance. We combine these two high-level objectives into one and define the BalancedTA problem. We also consider a generalization of this problem where each task consists of required and optional skills. In this setting, our objective is the same under the constraint that all required skills should be covered. From the technical point of view, we show that the BalancedTA problem (and its variant) is NP-hard and design efficient heuristics for solving it in practice. Using real datasets from three online market places, Freelancer, Guru and Upwork we demonstrate the efficiency of our methods and the practical utility of our framework.

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“…A large body of work in team formation considers the following problem: given a social or a collaboration network among the workers and a set of skills that needed to be covered, select a team of experts that can collectively cover all the required skills, while minimizing the communication cost between the team members [2,4,10,11,16,18,19,27]. Other variants of this problem have also considered optimizing the cost of recruiting promising candidates for a set of pre-defined tasks in an offline fashion [12] and minimizing the workload assigned to each individual team member [3,21].…”

Section: Related Workmentioning

confidence: 99%

Algorithms for Hiring and Outsourcing in the Online Labor Market

Anagnostopoulos

Castillo

Fazzone

et al. 2018

Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

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Although freelancing work has grown substantially in recent years, in part facilitated by a number of online labor marketplaces, traditional forms of "in-sourcing" work continue being the dominant form of employment. This means that, at least for the time being, freelancing and salaried employment will continue to co-exist. In this paper, we provide algorithms for outsourcing and hiring workers in a general setting, where workers form a team and contribute different skills to perform a task. We call this model team formation with outsourcing. In our model, tasks arrive in an online fashion: neither the number nor the composition of the tasks are known a-priori. At any point in time, there is a team of hired workers who receive a fixed salary independently of the work they perform. This team is dynamic: new members can be hired and existing members can be fired, at some cost. Additionally, some parts of the arriving tasks can be outsourced and thus completed by nonteam members, at a premium. Our contribution is an efficient online cost-minimizing algorithm for hiring and firing team members and outsourcing tasks. We present theoretical bounds obtained using a primal-dual scheme proving that our algorithms have logarithmic competitive approximation ratio. We complement these results with experiments using semi-synthetic datasets based on actual task requirements and worker skills from three large online labor marketplaces.

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Profit-maximizing cluster hires

Cited by 28 publications

References 15 publications

An Efficient Framework for Balancing Submodularity and Cost

An Efficient Framework for Balancing Submodularity and Cost

Finding teams that balance expert load and task coverage

Algorithms for Hiring and Outsourcing in the Online Labor Market

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