Abstract:
Under the explosive increase of global data,
the term Big data is mainly used to describe
enormous datasets. With the availability of
increasingly large quantities of digital
information, it is becoming more difficult for
researchers to extract and find relevant articles
pertinent to their interests. In this system, we
propose an approach to discover and
recommend the desired articles by combining
collaborative filtering (CF) with topic modeling.
Correlated Topic Model (CTM) is used for
modeling topics. Our approach not only
considers the interactions between users through
collaborative filtering but also learns the
properties of items involved through topic
modeling to improve recommendation. In order
to handle a large dataset, a Big data analytics
tool Hadoop is used to perform processing over
distributed clusters. The proposed approach
learns the accuracy of the recommendation.
