Volume 8, Issue 2 (9-2021)
Human Information Interaction 2021, 8(2): 53-66 |
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refoua S, salimi Z. Performance Evaluation of the Recommender System in Scientific Databases. Human Information Interaction 2021; 8 (2)
URL: http://hii.khu.ac.ir/article-1-2943-en.html
URL: http://hii.khu.ac.ir/article-1-2943-en.html
Abstract: (3579 Views)
Background and Aim: Scientific article recommender system assists and advance information retrieval process by proposing and offering articles tailored to the researchers needs. The main purpose of this study is to evaluate the performance of the recommender System in three scientific databases.
Method: This applied study is directed by the valuation method. Sample consisted of three scientific databases: Elsevier, Taylor & Francis, and Google Scholar, which share recommendation tools. "Information storage and retrieval" was selected as the search subject. Ten specialized keywords related to the topic of information storage and retrieval were selected. After searching each key words, the first retrieved article was reviewed. Then, for each first article, the first 5 recommended articles were mined in each of the three mentioned databases. Data was collected through direct observation using a researcher-made checklist. To evaluate subject relevance, bibliographic information of the first article retrieved in each subject and database along with the bibliographic information of 5 recommended articles was provided to two groups of librarians and IT professionals. Sample was selected by snowball method. Descriptive and inferential statistics were used to analyze the data.
Results: Findings showed that among the databases, Elsevier recommends more relevant results from the perspective of IT professionals and librarians in the field of information storage and retrieval, with Google Scholar and Taylor & Francis in the next ranks. In total, the most relevant articles in terms of subject experts were the articles that ranked fifth.
Conclusion: To sum up, Elsevier performed better than the other two databases in terms of recommending related articles. Also, there is a significant difference between the views of librarians and IT professionals regarding the relevance of recommended articles in the field of information storage and retrieval. Thus, from the point of view of IT professionals, the significance of the recommended articles is greater.
Method: This applied study is directed by the valuation method. Sample consisted of three scientific databases: Elsevier, Taylor & Francis, and Google Scholar, which share recommendation tools. "Information storage and retrieval" was selected as the search subject. Ten specialized keywords related to the topic of information storage and retrieval were selected. After searching each key words, the first retrieved article was reviewed. Then, for each first article, the first 5 recommended articles were mined in each of the three mentioned databases. Data was collected through direct observation using a researcher-made checklist. To evaluate subject relevance, bibliographic information of the first article retrieved in each subject and database along with the bibliographic information of 5 recommended articles was provided to two groups of librarians and IT professionals. Sample was selected by snowball method. Descriptive and inferential statistics were used to analyze the data.
Results: Findings showed that among the databases, Elsevier recommends more relevant results from the perspective of IT professionals and librarians in the field of information storage and retrieval, with Google Scholar and Taylor & Francis in the next ranks. In total, the most relevant articles in terms of subject experts were the articles that ranked fifth.
Conclusion: To sum up, Elsevier performed better than the other two databases in terms of recommending related articles. Also, there is a significant difference between the views of librarians and IT professionals regarding the relevance of recommended articles in the field of information storage and retrieval. Thus, from the point of view of IT professionals, the significance of the recommended articles is greater.
Keywords: Research Paper Recommender System, Recommender Systems, Database Recommender System, Subject Relevance, Scientific Databases, Google scholar, Elsevier, Taylor & Francis.
References
1. Adomavicius, G. & Tuzhilin, A. (2011). Context-Aware Recommender Systems. Context-aware recommender systems. in Recommender systems handbook, 217-253. [DOI:10.1007/978-0-387-85820-3_7]
2. Bauer, J. & Nanopoulos, A. (2014). Recommender systems based on quantitative implicit customer feedback. Decision Support Systems,68,77-88. [DOI:10.1016/j.dss.2014.09.005]
3. Beel, J., Gipp, B., Langer, S. & Breitinger, C. (2016). Research-paper recommender systems: a literature survey. International Journal on Digital Libraries, 17(4), 305-338. [DOI:10.1007/s00799-015-0156-0]
4. Costa, A., & Roda, F. (2011). Recommender Systems by means of Information Retrieval. in Proceedings of the International Conference on Web Intelligence, Mining and Semantics. [DOI:10.1145/1988688.1988755]
5. Dehghani Champiri Z, & Saeedbakhsh S. (2018). An Architecture for Scholarly Recommender System Based on Identified Contextual Information in Medical Sciences. Journal of Health Administration, 21 (71) :79-93. (Persian)
6. Ghasemi alvari, M. & Abbasi dashtaki, N. (2016). Compare the performance of the suggestion tool in Google, Yahoo and Bing search engines, 4(16), 75-96. (Persian)
7. Gipp ,B., Beel, J., & Hentschel, C. (2009). Scienstein: A Research Paper Recommender System. in Proceedings of the International Conference on Emerging Trends in Computing , 309-315.
8. Lee, J., Lee, K., & Kim, J. G. (2013) .Personalized Academic Research Paper Recommendation System. arXiv.
9. Hariri, N. (2011). Relevance ranking on google: are top ranked results really considered more relevant by the users?. online information review,35(4), 598-610. [DOI:10.1108/14684521111161954]
10. Haruna, K., Ismail, M.A., Qazi, A. Adamu Kakudi, H., Hassan, M., & et al. (2020). Research paper recommender system based on public contextual metadata. Scientometrics, 125 ,101-114. [DOI:10.1007/s11192-020-03642-y]
11. Khosravi, A., Fattahi, F., Parirokh, M., & Dayyani, M. (2013).The Efficacy of Google's suggested keywords and phrases in Query Expansion on postgraduates' View about retrieval relevance. Library and Information Science Research Journal, 3 (1), 133-148. (Persian)
12. Khosravi, A., Poosh, Z., & Arastoupour, S. (2015). The Efficiency of Pubmed Query Refinement Suggestions in Comparison with MESH Terms: A Bushehr Medical Specialists Viewpoint. Iranian Journal of Information processing and Management, 30 (3) :697-717.(Persian)
13. Lu, J., Wu, D., Wang, W., & Zhang, G. (2015). Recommender system application developments: A survey. Decision Support Systems, 74,12-32. [DOI:10.1016/j.dss.2015.03.008]
14. Matsatsinis, N., Lakiotaki, K., & Delias, P. (2007). A System based on Multiple Criteria Analysis for Scientific Paper Recommendation. in Proceedings of the 11th Panhellenic Conference in Informatics.
15. Ostendorff, M. (2020). Contextual Document Similarity for Content-based Literature Recommender Systems. in Proceedings of Doctoral Consortium at the ACM/IEEE Joint Conference on Digital Libraries (JCDL 2020).
16. Porcel, C., Tejeda- Lorente, A., Martinez, M.A., & Herrera-Viedma, E. (2012). A hybrid recommender system for the selective dissemination of research resources in a Technology Transfer Office. Information Sciences, 184,1-19. [DOI:10.1016/j.ins.2011.08.026]
17. Pruitikanee, S. , Di Jorio, L., Laurent, A. , & Sala, M. (2013). Paper Recommendation System: A Global and Soft Approach. Future Computing '2012: Fourth International Conference on Future Computational Technologies and Applications, Jun 2012.
18. Sadein, S., & Abbaspour, J. (2018a). Article Ranking by Recommender Systems vs. Users' Perspectives. Journal of National Studies on Librarianship and Information Organization, 3 (119), 46-57. (Persian)
19. Sadein, S., & Abbaspour, J. (2018b). Comparing the effectiveness of related articles recommender systems in Web of Science and Google Scholar. Journal of Academic Librarianship and Information Research, 53 (1). (Persian)
20. Sakib, N., Ahmad, R. B., & Haruna, K. (2020). A collaborative approach toward scientific paper recommendation using citation context. IEEE Access, 8, 51246-51255. [DOI:10.1109/ACCESS.2020.2980589]
21. Shahbazi, M., & Shahini, S. (2016). Study of the the efficacy Magiran, Noormags and SID database in retrieval and relevance of Information Science and Knowledge subject by free keywords and Compare them in terms of the use of controlled keywords. Iranian Journal of Information processing and Management, 31 (2),431-454. (Persian)
22. Tejeda-Lorente, A., Porcel, C., Peis, E., Sanz, R., & Herrera-Viedma, E. (2014). A quality based recommender system to disseminate information in a university digital library. Information Sciences,261,52-69. [DOI:10.1016/j.ins.2013.10.036]
23. Vellino, A. , & Zeber, D. (2007). A Hybrid, Multi-dimensional Recommender for Journal Articles in a Scientific Digital Library. Proceedings of the 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology, 111 - 114. [DOI:10.1109/WI-IATW.2007.29]
24. Watanabe, S., Ito, T., Ozono, T., & Shintani, T. (2011). A Paper Recommendation Mechanism for the Research Support System Papits. in Proceedings of International Workshop on Data Engineering Issues in E-Commerce, 71-80.
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