Collaborative team formation using brain drain optimization: a practical and effective solution

Basiri, Javad; Taghiyareh, Fattaneh; Ghorbani, Amineh

doi:10.1007/s11280-017-0440-6

Cited by 25 publications

(12 citation statements)

References 34 publications

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“…Taghiyareh et al also proposed a swarm intelligent Brain Drain Optimization (BRADO) to find a team of experts in DBLP and IMDB datasets. Their results were effective PSO, GA, RarestFirst, and EnhancedSteiner algorithms [ 5 ].…”

Section: Related Workmentioning

confidence: 99%

“…In 2009, Lappas et al tackled team formation and tried to find expert teams that can fulfill all tasks with minimum communication cost. They called TF an NP hard problem because no polynomial-time algorithm has been able to solve it [ 4 , 5 ]. Existing approaches tried to identify teams with minimum communication costs, balanced workloads, personnel costs, and team reliability, unique experts, or all of them combined.…”

Section: Introductionmentioning

confidence: 99%

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A novel state space reduction algorithm for team formation in social networks

et al. 2021

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Team formation (TF) in social networks exploits graphs (i.e., vertices = experts and edges = skills) to represent a possible collaboration between the experts. These networks lead us towards building cost-effective research teams irrespective of the geolocation of the experts and the size of the dataset. Previously, large datasets were not closely inspected for the large-scale distributions & relationships among the researchers, resulting in the algorithms failing to scale well on the data. Therefore, this paper presents a novel TF algorithm for expert team formation called SSR-TF based on two metrics; communication cost and graph reduction, that will become a basis for future TF’s. In SSR-TF, communication cost finds the possibility of collaboration between researchers. The graph reduction scales the large data to only appropriate skills and the experts, resulting in real-time extraction of experts for collaboration. This approach is tested on five organic and benchmark datasets, i.e., UMP, DBLP, ACM, IMDB, and Bibsonomy. The SSR-TF algorithm is able to build cost-effective teams with the most appropriate experts–resulting in the formation of more communicative teams with high expertise levels.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A novel state space reduction algorithm for team formation in social networks

et al. 2021

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“…Although high homophily can make team communications easier, it may not necessarily contribute to deriving novel, innovative ideas due to the nature of similarity in the ideas/thoughts. Basiri et al [25] tackled the same TF problem but used a meta-heuristic algorithm, called BRADO (BRAin Drain Optimization) [26], which is a type of swarm algorithms. Wang et al [27] conducted a comprehensive performance comparison of the major TF algorithms based on the proposed benchmark for fair comparison.…”

Section: Chapter 2 Backgroundmentioning

confidence: 99%

PRADA-TF: Privacy-Diversity-Aware Online Team Formation

Mahajan

Cho

2021

2021 IEEE International Conference on Web Services (ICWS)

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“…Team formation is an important but much neglected aspect of collaborative games, yet it is a widely-explored topic in non-gaming domains, such as in collaborative filtering and recommendation (Retna Raj and Sasipraba 2015;Ghenname et al 2015;Najafabadi and Mahrin 2016), collaborative design (Xu et al 2010), collaborative crowdsourcing (Lykourentzou et al 2016), and expert collaboration in social networks (Basiri et al 2017). However, in multi-player games, the focus has overwhelmingly been on player matching and more so with a view to finding worthy opponents for players to play against rather than suitable players to play with in a team (Daylamani Zad et al 2012).…”

Section: Advantages and Challenges Of Collaborative Gamesmentioning

confidence: 99%

Reflective agents for personalisation in collaborative games

2018

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The collaborative aspect of games has been shown to potentially increase player performance and engagement over time. However, collaborating players need to perform well for the team as a whole to benefit and thus teams often end up performing no better than a strong player would have performed individually. Personalisation offers a means for improving overall performance and engagement, but in collaborative games, personalisation is seldom implemented, and when it is, it is overwhelmingly passive such that the player is not guided to goal states and the effectiveness of the personalisation is not evaluated and adapted accordingly. In this paper, we propose and apply the use of reflective agents to personalisation ('reflective personalisation') in collaborative gaming for individual players within collaborative teams via a combination of individual player and team profiling in order to improve player and thus team performance and engagement. The reflective agents self-evaluate, dynamically adapting their personalisation techniques to most effectively guide players towards specific goal states, match players and form teams. We incorporate this agent-based approach within a microservices architecture, which itself is a set of collaborating services, to facilitate a scalable and portable approach that enables both player and team profiles to persist across multiple games. An experiment involving 90 players over a two-month period was used to comparatively assess three versions of a collaborative game that implemented reflective, guided, and passive personalisation for individual players within teams. Our results suggest that the proposed reflective personalisation approach improves team player performance and engagement within collaborative games over guided or passive personalisation approaches, but that it is especially effective for improving engagement.

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Collaborative team formation using brain drain optimization: a practical and effective solution

Cited by 25 publications

References 34 publications

A novel state space reduction algorithm for team formation in social networks

A novel state space reduction algorithm for team formation in social networks

PRADA-TF: Privacy-Diversity-Aware Online Team Formation

Reflective agents for personalisation in collaborative games

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