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Recommending research articles: A multi-level chronological learning-based approach using unsupervised keyphrase extraction and lexical similarity calculation

A research article recommendation approach aims to recommend appropriate research articles to analogous researchers to help them better grasp a new topic in a particular research area. Due to the accessibility of research articles on the web, it is tedious to recommend a relevant article to a resear...

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Main Authors: Sarwar, T. B., Noor, N. M., Saef Ullah Miah, M., Rashid, M., Farid, F.A., Husen, M. N.
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers Inc. 2021
Subjects:
QA76 Computer software
T Technology (General)
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://umpir.ump.edu.my/id/eprint/32852/1/Recommending%20research%20articles_a%20multi-level%20chronological%20learning-based%20approach%20using%20unsupervised.pdf
http://umpir.ump.edu.my/id/eprint/32852/
https://doi.org/10.1109/ACCESS.2021.3131470
https://doi.org/10.1109/ACCESS.2021.3131470
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spelling my.ump.umpir.328522022-03-24T02:34:25Z http://umpir.ump.edu.my/id/eprint/32852/ Recommending research articles: A multi-level chronological learning-based approach using unsupervised keyphrase extraction and lexical similarity calculation Sarwar, T. B. Noor, N. M. Saef Ullah Miah, M. Rashid, M. Farid, F.A. Husen, M. N. QA76 Computer software T Technology (General) TK Electrical engineering. Electronics Nuclear engineering A research article recommendation approach aims to recommend appropriate research articles to analogous researchers to help them better grasp a new topic in a particular research area. Due to the accessibility of research articles on the web, it is tedious to recommend a relevant article to a researcher who strives to understand a particular article. Most of the existing approaches for recommending research articles are metadata-based, citation-based, bibliographic coupling-based, content-based, and collaborative filtering-based. They require a large amount of data and do not recommend reference articles to the researcher who wants to understand a particular article going through the reference articles of that particular article. Therefore, an approach that can recommend reference articles for a given article is needed. In this paper, a new multi-level chronological learning-based approach is proposed for recommending research articles to understand the topics/concepts of an article in detail. The proposed method utilizes the TeKET keyphrase extraction technique, among other unsupervised techniques, which performs better in extracting keyphrases from the articles. Cosine and Jaccard similarity measures are employed to calculate the similarity between the parent article and its reference articles using the extracted keyphrases. The cosine similarity measure outperforms the Jaccard similarity measure for finding and recommending relevant articles to understand a particular article. The performance of the recommendation approach seems satisfactory, with an NDCG value of 0.87. The proposed approach can play an essential role alongside other existing approaches to recommend research articles. Author Institute of Electrical and Electronics Engineers Inc. 2021-12-10 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/32852/1/Recommending%20research%20articles_a%20multi-level%20chronological%20learning-based%20approach%20using%20unsupervised.pdf Sarwar, T. B. and Noor, N. M. and Saef Ullah Miah, M. and Rashid, M. and Farid, F.A. and Husen, M. N. (2021) Recommending research articles: A multi-level chronological learning-based approach using unsupervised keyphrase extraction and lexical similarity calculation. IEEE Access, 9. pp. 160797-160811. ISSN 2169-3536 https://doi.org/10.1109/ACCESS.2021.3131470 https://doi.org/10.1109/ACCESS.2021.3131470
institution Universiti Malaysia Pahang
building UMP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang
content_source UMP Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic QA76 Computer software
T Technology (General)
TK Electrical engineering. Electronics Nuclear engineering
spellingShingle QA76 Computer software
T Technology (General)
TK Electrical engineering. Electronics Nuclear engineering
Sarwar, T. B.
Noor, N. M.
Saef Ullah Miah, M.
Rashid, M.
Farid, F.A.
Husen, M. N.
Recommending research articles: A multi-level chronological learning-based approach using unsupervised keyphrase extraction and lexical similarity calculation
description A research article recommendation approach aims to recommend appropriate research articles to analogous researchers to help them better grasp a new topic in a particular research area. Due to the accessibility of research articles on the web, it is tedious to recommend a relevant article to a researcher who strives to understand a particular article. Most of the existing approaches for recommending research articles are metadata-based, citation-based, bibliographic coupling-based, content-based, and collaborative filtering-based. They require a large amount of data and do not recommend reference articles to the researcher who wants to understand a particular article going through the reference articles of that particular article. Therefore, an approach that can recommend reference articles for a given article is needed. In this paper, a new multi-level chronological learning-based approach is proposed for recommending research articles to understand the topics/concepts of an article in detail. The proposed method utilizes the TeKET keyphrase extraction technique, among other unsupervised techniques, which performs better in extracting keyphrases from the articles. Cosine and Jaccard similarity measures are employed to calculate the similarity between the parent article and its reference articles using the extracted keyphrases. The cosine similarity measure outperforms the Jaccard similarity measure for finding and recommending relevant articles to understand a particular article. The performance of the recommendation approach seems satisfactory, with an NDCG value of 0.87. The proposed approach can play an essential role alongside other existing approaches to recommend research articles. Author
format Article
author Sarwar, T. B.
Noor, N. M.
Saef Ullah Miah, M.
Rashid, M.
Farid, F.A.
Husen, M. N.
author_facet Sarwar, T. B.
Noor, N. M.
Saef Ullah Miah, M.
Rashid, M.
Farid, F.A.
Husen, M. N.
author_sort Sarwar, T. B.
title Recommending research articles: A multi-level chronological learning-based approach using unsupervised keyphrase extraction and lexical similarity calculation
title_short Recommending research articles: A multi-level chronological learning-based approach using unsupervised keyphrase extraction and lexical similarity calculation
title_full Recommending research articles: A multi-level chronological learning-based approach using unsupervised keyphrase extraction and lexical similarity calculation
title_fullStr Recommending research articles: A multi-level chronological learning-based approach using unsupervised keyphrase extraction and lexical similarity calculation
title_full_unstemmed Recommending research articles: A multi-level chronological learning-based approach using unsupervised keyphrase extraction and lexical similarity calculation
title_sort recommending research articles: a multi-level chronological learning-based approach using unsupervised keyphrase extraction and lexical similarity calculation
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2021
url http://umpir.ump.edu.my/id/eprint/32852/1/Recommending%20research%20articles_a%20multi-level%20chronological%20learning-based%20approach%20using%20unsupervised.pdf
http://umpir.ump.edu.my/id/eprint/32852/
https://doi.org/10.1109/ACCESS.2021.3131470
https://doi.org/10.1109/ACCESS.2021.3131470
_version_ 1729703430976438272
score 13.211869

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