Please use this identifier to cite or link to this item:

A Multi-criteria Decision Support System for Ph.D. Supervisor Selection: A Hybrid Approach

File Size Format  
0182.pdf 912 kB Adobe PDF View/Open

Item Summary

Title:A Multi-criteria Decision Support System for Ph.D. Supervisor Selection: A Hybrid Approach
Authors:Hasan, Mir Anamul
Schwartz, Daniel
Keywords:Multi-criteria Decision Analysis and Support Systems
Decision Analytics, Mobile Services, and Service Science
Academic Search, Expert Search, Fuzzy AHP, MCDM, Ph.D. Supervisor Selection
Date Issued:08 Jan 2019
Abstract:Selection of a suitable Ph.D. supervisor is a very important step in a student’s career. This paper presents a multi-criteria decision support system to assist students in making this choice. The system employs a hybrid method that first utilizes a fuzzy analytic hierarchy process to extract the relative importance of the identified criteria and sub-criteria to consider when selecting a supervisor. Then, it applies an information retrieval-based similarity algorithm (TF/IDF or Okapi BM25) to retrieve relevant candidate supervisor profiles based on the student’s research interest. The selected profiles are then re-ranked based on other relevant factors chosen by the user, such as publication record, research grant record, and collaboration record. The ranking method evaluates the potential supervisors objectively based on various metrics that are defined in terms of detailed domain-specific knowledge, making part of the decision making automatic. In contrast with other existing works, this system does not require the professor’s involvement and no subjective measures are employed.
Pages/Duration:10 pages
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
Appears in Collections: Multi-criteria Decision Analysis and Support Systems

Please email if you need this content in ADA-compliant format.

This item is licensed under a Creative Commons License Creative Commons