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