  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.
