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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Institute of Electrical and Electronics Engineers Inc.
2021
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| Online Access: | http://umpir.ump.edu.my/id/eprint/32852/ http://umpir.ump.edu.my/id/eprint/32852/1/Recommending%20research%20articles_a%20multi-level%20chronological%20learning-based%20approach%20using%20unsupervised.pdf |
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| 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. |
| building | UMP Institutional Repository |
| collection | Online Access |
| 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 |
| first_indexed | 2025-11-15T03:08:04Z |
| format | Article |
| id | ump-32852 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:08:04Z |
| publishDate | 2021 |
| publisher | Institute of Electrical and Electronics Engineers Inc. |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-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. (Published) https://doi.org/10.1109/ACCESS.2021.3131470 https://doi.org/10.1109/ACCESS.2021.3131470 |
| 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 |
| title | 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_short | 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 |
| topic | QA76 Computer software T Technology (General) TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/32852/ http://umpir.ump.edu.my/id/eprint/32852/ http://umpir.ump.edu.my/id/eprint/32852/ http://umpir.ump.edu.my/id/eprint/32852/1/Recommending%20research%20articles_a%20multi-level%20chronological%20learning-based%20approach%20using%20unsupervised.pdf |