Affiliation:
1. Department of Information Technology Henan University of Chinese Medicine Zhengzhou China
2. Department of Business Zhengzhou University of Aeronautics Zhengzhou China
Abstract
AbstractThe current personalized recommendation methods for teaching resources of university courses suffer from poor recommendation effectiveness due to the absence of user tags. To address this issue, a new personalized recommendation method based on cluster analysis is proposed. The proposed method leverages web crawler technology to obtain user tags, followed by processing the tags to remove meaningless terms, normalize word forms, and perform data processing. The processed tags are used to calculate user interest preferences for each tag cluster generated by clustering. Based on this, a user interest model is built, and user similarity is calculated to determine the recommendation score of each resource. The recommended resources are then ranked according to their recommendation score and presented to the target user. Experimental results demonstrate that the proposed method achieves high accuracy, recall rate, and F1 value for personalized recommendation of teaching resources in colleges and universities. In comparison, the method proposed in this paper has a significantly shorter recommendation time of 10.65 s. Further, the proposed model not only takes less time but also has higher recommendation efficiency when compared with existing personalized recommendation methods.
Subject
General Engineering,Education,General Computer Science
Reference27 articles.
1. Research on personalized recommendation methods for online video learning resources;Chen X.;Appl. Sci,2021
2. An Urban Network Study of Government Procurement Activities: A Case Study of Northeast China
3. TESS: multivariate sensor time series prediction for building sustainable smart cities
4. Research on mobile learning resource recommendation model based on learners' spatial and temporal characteristics[J];Li H.;Modern Educ. Technol,2020
5. Personalized learning resource recommendation method based on three‐dimensional feature cooperative Domination;Li H.;Comp. Sci,2019