hosseinfani
commented
2 years ago @Rounique Nice summary. It lacks some parts though: gaps of previous work, gaps of this work, code, and evaluation More importantly, where the fairness is applied in the proposed two methods!
Title: Fair Team Recommendations for Multidisciplinary Projects Venue: WI Year: 2019
Introduction: Recommender systems could be quite beneficial in diverse team-building challenges for projects. The amount of needed human work can be greatly decreased when there are thousands of candidates and dozens of teams to form. Furthermore, manually maintaining the fairness aspect amongst teams is a difficult chore, which the recommender system algorithm can handle.
This paper focuses on the problem of team recommendation, in which teams must meet multidisciplinary requirements and team members are chosen based on how well their talents match the needs. When building numerous teams, it's also difficult to evenly distribute the best members throughout the teams. There are two strategies presented in this paper: 1)Brute Force 2)A Faster Heuristic
Main concern: If numerous teams are formed, the top candidates may be assigned to the first team, leaving the less qualified individuals for the other projects. Therefore, to allocate good members to all teams, the fairness element of this team formation should be considered as well.
Dataset: The dataset is a pre-processed version of DBLP that has 7428 lines. Each line corresponds to a researcher and contains the person's name and a varying number of skill tags related to that person. Each researcher has at least one skill and there are 4480 unique skills among all people.
Methods:
Brute Force Algorithm: From the set of available applicants A, all feasible team combinations T of k members are created for each project p in the set of projects P. The scoreTP function is then used to calculate the score for each possible team t in T in relation to the project p. (t, p). The team with the highest score is selected as the best team for that project, and its members are withdrawn from the pool of candidates A. For each project, the process of forming a team is repeated.
Heuristic Algorithm: Due to the computation of all possible team combinations, the brute force approach is computationally expensive. As a result, a heuristic is proposed that can be used to reduce calculations while maintaining suggestion efficacy. Rather than producing all possible team combinations, our heuristic generates scoreAP(a, p) between each applicant an in the set A and each project p in the set of projects P. The candidate a with the highest scoreAP for a specific project p is then selected as a member of that project team and removed from the pool of applicants A. This procedure is done k times until the project p team has selected all of its k members. The procedure of forming teams is then repeated for all of the other projects in the set P.
Future Work: Defining and adding new aspects of fairness to it, so that we can form more productive and diverse teams at the same time.