Construction and Push of Financial Instructional Resource Bank Based on Rough Set Theory

• Published in: Mobile information systems Vol. 2022; pp. 1 - 9
• Main Author: Li, Chang Zheng
• Format: Journal Article
• Language: English
• Published: Amsterdam Hindawi 21.07.2022; Hindawi Limited
• Subjects: Academic libraries; Algorithms; Approximation; Clustering; Collaboration; Colleges & universities; Construction; Data mining; Education; Knowledge management; Learning; Recommender systems; Set theory; Similarity; Sparsity; Teaching
• ISSN: 1574-017X; 1875-905X
• DOI: 10.1155/2022/3560590

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.