Journal recommendation system for author using Thai and English information from manuscript

Name: Nithirun Numnonda

Organization : Chulalongkorn University. Faculty of Engineering

Email : 6270142321@student.chula.ac.th

Name: Sapa Chanyachatchawan

Organization : National Electronics and Computer Technology Center (NECTEC)

Email : sapa.cha@nectec.or.th

Name: Nuengwong Tuaycharoen

Organization : Chulalongkorn University. Faculty of Engineering

Email : nuengwong.t@chula.ac.th

LCSH: Recommender systems (Information filtering)

LCSH: Information storage and retrieval systems -- Electronic journals

Abstract: There are thousands of academic journals in various fields of study. An article author must spend significant time searching and selecting a journal suitable for the article’s content before submitting it to a journal for consideration. Since many articles are submitted to a journal at a time, it would take time for an editor to review, submit it to reviewers, and inform the results back to the author. If the article is rejected due to mismatched journal content, the author will spend more time to re-submit the article to another journal. Therefore, this research introduced a recommendation system to help the author choose an appropriate journal more effectively, based on TCI Thai Journals Online Database (ThaiJO). Data from Thai and English articles were used for analysis in this research. Our work involved studying the applied data, cleaning the data, and modeling, which included calculating the importance of text by Term Frequency - Inverse Document Frequency (TF-IDF), calculating similarity scores between articles and journals using Cosine Similarity and then ranking the scores to recommend the most suitable journal. This research experiment with modeling between a model from Thai data, a model from English data, and a model using both languages. The experiment shows that when we combine Thai and English keywords and abstract data, the accuracy in the form of hit rate is improved to 0.80650 from applying only English (0.78793) or Thai data (0.62888).

King Mongkut's University of Technology North Bangkok. Central Library

Address: BANGKOK

Email: library@kmutnb.ac.th

Created: 2022

Modified: 2022-12-27

Issued: 2022-12-27

บทความ/Article

application/pdf

BibliograpyCitation : In King Mongkut's University of Technology North Bangkok. Faculty of Information Technology. The 18th International Conference on Computing and Information Technology (IC2IT 2022) (pp.142-151). Cham, Switzerland : Springer

eng

©copyrights King Mongkut's University of Technology North Bangkok

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