Document Classification for Recommender Systems Using Graph Convolutional Networks
Classification; Convolutional graph neural networks; Cora dataset; Recommender systems
Graph based recommender systems have time and time again proven their efficacy in the recommendation of scientific articles. But it is not without its challenges, one of the major ones being that these models consider the network for recommending while the class and domain of the article go unnoticed. The networks that embed the metadata and the network have highly scalable issues. Hence the identification of an architecture that is scalable and which operates directly on the graph structure is crucial to its amelioration. This study analyses the accuracy and efficiency of the Graph Convolutional Networks (GCN) on Cora Dataset in classifying the articles based on the citations and class of the article. It aims to show that GCN based networks provide a remarkable accuracy in classifying the articles. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Lecture Notes in Networks and Systems, Vol-290, pp. 403-410.
Springer Science and Business Media Deutschland GmbH
Nair A.M., Christ University, Bangalore, India; George J., Christ University, Bangalore, India