Research on Personalized Recommendation Based on Improved Whale Optimization Algorithm

• Published in: 2024 4th International Symposium on Computer Technology and Information Science (ISCTIS) pp. 695 - 698
• Main Authors: Jiang, Jun, Qing, Wenyang, Hu, Jun, Hou, Fazhong
• Format: Conference Proceeding
• Language: English
• Published: IEEE 12.07.2024
• Subjects: Accuracy; Clustering algorithms; cognitive diagnosis; component; Data mining; Data models; Information science; Optimization; Optimization models; Periodic structures; Probability distribution; Recommended test questions; whale optimization algorithm; Whale optimization algorithms
• DOI: 10.1109/ISCTIS63324.2024.10698972

In order to solve the problem of insufficient information mining and low degree of personalization due to the single data dimension of cognitive diagnosis test question recommendation, this paper proposes a personalized exercise question recommendation method based on multi-dimensional data cognitive diagnosis optimization model. Firstly, the improved whale optimization algorithm with adaptive probability distribution is used to solve the problem that the initial clustering center of the K-means algorithm is sensitive and easy to fall into the local optimum, and secondly, the optimized k-means algorithm was used to cluster the exercises to generate a specific group of test questions. Then, the students' answers to specific test groups were recorded to form a multi-dimensional test question set. Finally, based on the multi-dimensional question bank, the method proposed in this paper and the traditional GDINA cognitive model algorithm are used to test the average accuracy of student sampling, and the results show that the algorithm proposed in this paper has stronger information mining ability and is more suitable for accurate recommendation of personalized test question resources matching learning needs and abilities.