Construction and Push of Financial Instructional Resource Bank Based on Rough Set Theory
A perfect resource bank for financial education that can foster resource exchange and charitable work must be established. The traditional recommendation algorithm is improved in this paper based on the rough set theory, avoiding the issue where the traditional similarity calculation does not match...
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Published in | Mobile information systems Vol. 2022; pp. 1 - 9 |
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Main Author | |
Format | Journal Article |
Language | English |
Published |
Amsterdam
Hindawi
21.07.2022
Hindawi Limited |
Subjects | |
Online Access | Get full text |
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Abstract | A perfect resource bank for financial education that can foster resource exchange and charitable work must be established. The traditional recommendation algorithm is improved in this paper based on the rough set theory, avoiding the issue where the traditional similarity calculation does not match the actual similarity judgement. When used in the field of resource clustering for financial education, the improved algorithm has the concept of an approximate set for boundary problems. It can more accurately describe the boundary groups of resource classification. When the model is offline, a rough K-means user clustering algorithm is used; users are assigned to the upper and lower approximations of K classes based on how similar they are to cluster centers, creating the initial neighbor set of users. Find the target user’s nearest neighbor from the initial nearest neighbor set online, predict the item’s score, and offer suggestions to it. The evaluation’s findings indicate that this system can achieve a precision rate of 94.35 percent. Additionally, compared to the conventional method, the recommended recall rate is higher at 95.94 percent. This algorithm can effectively provide high-quality resources for financial teaching while overcoming the drawbacks of traditional recommendation algorithms, increasing recommendation accuracy. |
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AbstractList | A perfect resource bank for financial education that can foster resource exchange and charitable work must be established. The traditional recommendation algorithm is improved in this paper based on the rough set theory, avoiding the issue where the traditional similarity calculation does not match the actual similarity judgement. When used in the field of resource clustering for financial education, the improved algorithm has the concept of an approximate set for boundary problems. It can more accurately describe the boundary groups of resource classification. When the model is offline, a rough K-means user clustering algorithm is used; users are assigned to the upper and lower approximations of K classes based on how similar they are to cluster centers, creating the initial neighbor set of users. Find the target user’s nearest neighbor from the initial nearest neighbor set online, predict the item’s score, and offer suggestions to it. The evaluation’s findings indicate that this system can achieve a precision rate of 94.35 percent. Additionally, compared to the conventional method, the recommended recall rate is higher at 95.94 percent. This algorithm can effectively provide high-quality resources for financial teaching while overcoming the drawbacks of traditional recommendation algorithms, increasing recommendation accuracy. |
Author | Li, Chang Zheng |
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Cites_doi | 10.1155/2021/8387277 10.1109/tcyb.2020.2977267 10.1111/avj.12195 10.1016/j.jbankfin.2014.11.009 10.1152/advan.00078.2011 10.1016/j.ins.2014.09.056 10.1007/s11036-020-01673-6 10.3233/jifs-189033 10.1016/j.apm.2012.12.009 10.1007/s00500-015-1826-y 10.1016/j.ijar.2016.11.001 10.1016/j.neucom.2020.07.064 10.3233/JIFS-189240 10.1016/j.compedu.2018.08.011 10.1016/j.compedu.2012.12.004 10.1093/elt/cct063 10.1021/ed500433c |
ContentType | Journal Article |
Copyright | Copyright © 2022 Chang Zheng Li. Copyright © 2022 Chang Zheng Li. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0 |
Copyright_xml | – notice: Copyright © 2022 Chang Zheng Li. – notice: Copyright © 2022 Chang Zheng Li. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0 |
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SubjectTerms | Academic libraries Algorithms Approximation Clustering Collaboration Colleges & universities Construction Data mining Education Knowledge management Learning Recommender systems Set theory Similarity Sparsity Teaching |
Title | Construction and Push of Financial Instructional Resource Bank Based on Rough Set Theory |
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