A Multiplex Graph for Recommending Multidimensional Documents
Abstract
Network Science has become a flourishing interest in the last
decades with the Big Data explosion. To improve multidimensional data
Recommendation Systems, multiplex graph structures are useful to cap-
ture various types of user interactions. We propose a graph database
approach to compute multiplex graphs which helps both manipulating
dimensions in a flexible way and enhancing expressiveness with algebra
to express manipulations on the multiplex graph. Applied operations
rely on graph algorithms to predict user interactions. We compare our
approach with the traditional matrix approach with Random Walk with
Restart. The study shows that combination of scores from layers of mul-
tiplex graphs provide important insights into user preferences, with most
configurations outperforming traditional matrix methods. This approach
provides a comprehensive analysis of multidimensional recommendation
strategies in multiplex graphs, which provides capabilities of managing
different dimensions for queries, paving the way for more sophisticated
and customized recommendation systems and its explicability.