Sofia Maria Nikolakaki

Sofia Maria Nikolakaki, Mingxiang Cai, Evimaria Terzi

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.

Sofia M Nikolakaki, Ogheneovo Dibie, Ahmad Beirami et al.

Competition is a primary driver of player satisfaction and engagement in multiplayer online games. Traditional matchmaking systems aim at creating matches involving teams of similar aggregated individual skill levels, such as Elo score or TrueSkill. However, team dynamics cannot be solely captured using such linear predictors. Recently, it has been shown that nonlinear predictors that target to learn probability of winning as a function of player and team features significantly outperforms these linear skill-based methods. In this paper, we show that using final score difference provides yet a better prediction metric for competitive balance. We also show that a linear model trained on a carefully selected set of team and individual features achieves almost the performance of the more powerful neural network model while offering two orders of magnitude inference speed improvement. This shows significant promise for implementation in online matchmaking systems.