Date of Award

8-31-2020

Document Type

Thesis

Degree Name

Master of Science in Data Science - (M.S.)

Department

Computer Science

First Advisor

Craig Gotsman

Second Advisor

James Geller

Third Advisor

Senjuti Basu Roy

Abstract

The importance of team formation has been realized since ages, but finding the most effective team out of the available human resources is a problem that persists to the date. Having members with complementary skills, along with a few must-have behavioral traits, such as trust and collaborativeness among the team members are the key ingredients behind team synergy and performance. This thesis designs and implements two different algorithms for the team formation problem using ideas adapted from the recommender systems literature. One of the proposed solutions uses the Glicko-2 rating system to rate the employees’ skills which can easily separate the skill ability and experience of the employees. The final contribution of this thesis is to build a system with ”plug-in” capability, meaning any new recommendation algorithm could be easily plugged in inside the system. Our extensive experimental analyses explore nuances of data sources, data storage methodologies, as well as characteristics of different recommendation algorithms with rating and ranking sub-systems.

Included in

Data Science Commons

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