Content Based Deep Factorization Framework for Scientific Article Recommender System
subject: Citation recommendation; Deep neural network; Factorization machines
description: With the advancement in technology and the tremendous number of citations available in the digital libraries, it has become difficult for the research scholars to find a relevant set of reference papers. The accelerating rate of scientific publications results in the problem of information overload because of which the scholars spend their 70% of the time finding relevant papers. A citation recommendation system resolves the issue of spending a good amount of time and other resources for collecting a set of papers by providing the user with personalised recommendations of the articles. Existing state of art models do not take high-low order feature interactions into consideration, due to which the recommendations are not up to the desired level of performance. In this paper, we propose a content-based model which combines Deep Neural Network (DNN) and Factorization Machines (FM) where no pre-trainings are required for providing the citation recommendations. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
creator: Nair A.M.; Anto O.; Shaji A.; George J.
source: Lecture Notes in Electrical Engineering, Vol-907, pp. 433-442.
publisher: Springer Science and Business Media Deutschland GmbH
date: 2022-01-01
rights: Restricted Access
relation: ISSN: 18761100; ISBN: 978-981194686-8
format: Online
language: English
type: Conference paper
coverage: Nair A.M., Department of Computer Science, Christ University, Bangalore, India; Anto O., Department of Computer Science, Christ University, Bangalore, India; Shaji A., Department of Computer Science, Christ University, Bangalore, India; George J., Department of Computer Science, Christ University, Bangalore, India