Abstract
In the era of information overloading, enormous scholarly data poses a
challenge in identifying potential authors for productive outcomes. Researchers
collaborate with fellow profiles to improve the eminence of Research and their
academic profiles. This chapter proposes a content-based recommender system to
generate author recommendations for collaborations that extracts the relevant keywords
from the titles of research papers using MapReduce. To specify author specialization,
the proposed technique comprises the feature extraction from the entire document using
Latent Dirichlet Allocation (LDA), followed by an influence model which generates
recommendations for the target authors. A ranking algorithm, such as TOPSIS is
implemented to get Top-N recommendations based on multiple criteria. In this chapter,
we investigated how the MapReduce framework is helpful in obtaining improved
computational time for large-scale scholarly data and scalability. Experimental results
on DBLP articles prove the relevance of ranking methods as an efficient and scalable
platform for computing content-based recommendations