In recent years the volume of data has increased significantly creating new challenges and
opportunities in dealing with the interconnected data. Although new technologies enable the
processing of high volumes of information, it is still challenging to find the relationships within
the data that realise the anticipated business value. Graph analysis is becoming increasingly
important to find the insights from connected data and to leverage machine learning outcomes.
This thesis presents graph analytics applied on the leading ACID compliant graph dbms Neo4j
to derive the features to improve on the prediction of recommender algorithms. The research
uses the Movielens dataset for benchmarking purposes. Python is used for building the data
pipeline using embedded cypher and python machine learning libraries.
The research demonstrates the effectiveness of link prediction as a method for derivation of the
features for machine learning. The resultant improvements in recall are demonstrated.