Open AccessHow to optimize Recommendation System Performance using Deep Neural Network based Graph Architecture
Author(s) -
Indranil Dutta
Publication year - 2022
Publication title -
international journal of scientific research and management
Language(s) - English
Resource type - Journals
ISSN - 2321-3418
DOI - 10.18535/ijsrm/v10i3.ec04
Subject(s) - computer science , architecture , artificial intelligence , collaborative filtering , graph , recommender system , artificial neural network , cloud computing , flexibility (engineering) , deep learning , matrix decomposition , factorization , theoretical computer science , machine learning , algorithm , operating system , mathematics , physics , quantum mechanics , visual arts , art , statistics , eigenvalues and eigenvectors
In this research, would bring a description and comparison of how the Deep learning-based Graph neural Network actually outperforms the other similar recommender system like collaborative filtering, content-based filtering, SVD, Matrix Factorization and few others. This is basically achieved by exposing the correct relation between the objects through a graph architecture and the dependency and inter correlation between them. Would also like to share an in-depth analysis and understanding of how the Graph architecture works and the underlying theories. This could be either a TensorFlow based architecture or a Pytorch based architecture but in this paper will mainly focus on the TensorFlow one for its flexibility and cloud friendly nature for adopting in any framework.