Document Classification for Recommender Systems Using Graph Convolutional Networks

Title

Document Classification for Recommender Systems Using Graph Convolutional Networks

Creator

Nair A.M.; George J.

Description

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.

Source

Lecture Notes in Networks and Systems, Vol-290, pp. 403-410.

Date

2021-01-01

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Classification; Convolutional graph neural networks; Cora dataset; Recommender systems

Coverage

Nair A.M., Christ University, Bangalore, India; George J., Christ University, Bangalore, India

Rights

Restricted Access

Relation

ISSN: 23673370; ISBN: 978-981164485-6

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-981-16-4486-3_45

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115083037&doi=10.1007%2f978-981-16-4486-3_45&partnerID=40&md5=8b036fe87faf829ae807de82316a5ef6