Graph Representation Learning for Recommender Systems

Graph data represents information efficiently and can be used to learn subsequent tasks easily. In the domain of biological science, recommender systems, social network analysis graph representation learning has become ubiquitous with tremendous success. A graph structured data can make a system to learn, produce new information and can be analyzed for set of tasks. Deep learning-based recommender systems are using deep graph embeddings for improved accuracy by exploring user item relations. This relation exploration can be utilized for other recommendation task such as link prediction. In this study we are providing overview of graph representation learning with complete analysis of data with graph representation. This study provides importance of graph representation in recommender system to learn node representations. The results and discussion section denotes how simple graph neural network technique successfully applied on graph data for node representation learning and challenges.