Multi-Layer Graph Generative Model Using AutoEncoder for Recommendation Systems

Given the glut of information on the web, it is crucially important to have a system, which will parse the information appropriately and recommend users with relevant information, this class of systems is known as Recommendation Systems (RS)-it is one of the most extensively used systems on the web today. Recently, Deep Learning (DL) models are being used to generate recommendations, as it has shown state-of-the-art (SoTA) results in the field of Speech Recognition and Computer Vision in the last decade. However, the RS is a much harder problem, as the central variable in the recommendation system’s environment is the chaotic nature of the human’s purchasing/consuming behaviors and their interest. These user-item interactions cannot be fully represented in the EuclideanSpace, as it will trivialize the interaction and undermine the implicit interactions patterns. So to preserve the implicit as well as explicit interactions of user and items, we propose a new graph based recommendation framework. The fundamental idea behind this framework is not only to generate the recommendations in the unsupervised fashion but to learn the dynamics of the graph and predict the short and long term interest of the users. In this paper, we propose the first step, a heuristic multi-layer high-dimensional graph which preserves the implicit and explicit interactions between users and items using SoTA Deep Learning models such as AutoEncoders. To generate recommendation from this generated graph a new class of neural network architecture-Graph Neural Network-can be used.