Vol.1, No.1, 2019, pp.1-7, doi:10.32604/jbd.2019.05899
OPEN ACCESS
ARTICLE
Multi-Layer Graph Generative Model Using AutoEncoder for Recommendation Systems
  • Syed Falahuddin Quadri1, Xiaoyu Li1,*, Desheng Zheng2, Muhammad Umar Aftab1, Yiming Huang3
School of Information and Software Engineering, University of Electronics Science and Technology of China, Chengdu, 610054, China.
School of Computer Science, Southwest Petroleum University, 8 Xindu Avenue, Chengdu, 610500, China.
University of Maryland Institute for Advanced Computer Studies, Bldg 224 Stadium Dr Room 3100, College Park, Maryland, 20742, USA.
*Corresponding Author: Xiaoyu Li. Email: xiaoyu33521@163.com.
Abstract
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 Euclidean-Space, 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.
Keywords
Recommendation systems, autoencoder, knowledge representation, representation learning, graph-structured data.
Cite This Article
Quadri, S. F., Li, X., Zheng, D., Aftab, M. U., Huang, Y. (2019). Multi-Layer Graph Generative Model Using AutoEncoder for Recommendation Systems. Journal on Big Data, 1(1), 1–7.