Institutional Collaboration Recommendation: An expertise-based framework using NLP and Network Analysis

TL;DR

This paper presents a novel NLP and network analysis-based framework for recommending institutional collaborations by identifying thematic strengths and core competencies, aiming to enhance institutional performance in research fields.

Contribution

The study introduces a new framework that recognizes thematic strengths of institutions for collaboration recommendations, filling a gap in existing systems.

Findings

Framework effectively identifies institutional strengths in thematic areas.

System provides novel and diverse collaboration recommendations.

Validated on 195 Indian institutions with positive coverage and diversity metrics.

Abstract

The shift from 'trust-based funding' to 'performance-based funding' is one of the factors that has forced institutions to strive for continuous improvement of performance. Several studies have established the importance of collaboration in enhancing the performance of paired institutions. However, identification of suitable institutions for collaboration is sometimes difficult and therefore institutional collaboration recommendation systems can be vital. Currently, there are no well-developed institutional collaboration recommendation systems. In order to bridge this gap, we design a framework that recognizes thematic strengths and core competencies of institutions, which can in turn be used for collaboration recommendations. The framework, based on NLP and network analysis techniques, is capable of determining the strengths of an institution in different thematic areas within a field and thereby determining the core competency and potential core competency areas of that institution. A major advantage of the system is that it can help to determine and improve the research portfolio of an institution within a field through suitable collaboration, which may lead to the overall improvement of the performance of the institution in that field. The framework is demonstrated by analyzing the performance of 195 Indian institutions in the field of 'Computer Science'. Upon validation using standard metrics for novelty, coverage and diversity of recommendation systems, the framework is found to be of sufficient coverage and capable of tossing novel and diverse recommendations. The article thus presents an institutional collaboration recommendation system which can be used by institutions to identify potential collaborators.