Scaling Network Embeddings

Abstract

A Recommendation System is an intelligent machine learning system that seeks to predict a customer ranked set of personalized products from a dynamic pool of diverse choices. We can define the main objective of such systems as ranking edges in an undirected unweighted graph consisting of user and item nodes.

Deep Graph embeddings have recently attracted the interests of both academia and industry, mainly because of its simplicity and effectiveness in a variety of applications. This thesis's primary purpose is to perform research on the existing graph embeddings methods for recommendation algorithms. We aim to transform undirected unweighted graphs into vectors, also known as graph embeddings, to make a representation that would be suitable for different machine learning algorithms. At first, we introduce the reader to some existing and conventional approaches that allow us to create such embeddings. We then present several modifications and improvements to the existing methods. Finally, we use several evaluation metrics to showcase the performance evaluations of such modifications.