Content Based Scientific Article Recommendation System Using Deep Learning Technique

Title

Content Based Scientific Article Recommendation System Using Deep Learning Technique

Creator

Nair A.M.; Benny O.; George J.

Description

The emergence of the era of big data has increased the ease with which scientific users can access academic articles with better efficiency and accuracy from a pool of papers available. With the exponential increase in the number of research papers that are getting published every year, it has made scholars face the problem of information overload where they find it difficult to conduct comprehensive literature surveys. An article recommendation system helps in overcoming this issue by providing users with personalized recommendations based on their interests and choices. The common approaches used for recommendation are Content-Based Filtering (CBF) and Collaborative Filtering (CF). Even though there is much advancement in the field of article recommendation systems, a content-based approach using a deep learning technology is still in its inception. In this work, a C-SAR model using Gated Recurrent Unit (GRU) and association rule mining Apriori algorithm to provide a recommendation of articles based on the similarity in the content were proposed. The combination of a deep learning technique along with a classical algorithm in data mining is expected to provide better results than the state-of-art model in suggesting similar papers. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Source

Lecture Notes in Networks and Systems, Vol-204 LNNS, pp. 965-977.

Date

2021-01-01

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Apriori algorithm; Content-Based recommendation; Gated recurrent unit

Coverage

Nair A.M., Department of Computer Science, CHRIST (Deemed to Be University), Lavasa, Pune, India; Benny O., Department of Computer Science, CHRIST (Deemed to Be University), Lavasa, Pune, India; George J., Department of Computer Science, CHRIST (Deemed to Be University), Lavasa, Pune, India

Rights

Restricted Access

Relation

ISSN: 23673370; ISBN: 978-981161394-4

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-981-16-1395-1_70

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111347166&doi=10.1007%2f978-981-16-1395-1_70&partnerID=40&md5=ecf5ffeac6b6829eea55c7d4b16afb02