Research Article
Fast Recommendation Method of Personalized Tourism Big Data Information Based on Improved Clustering Algorithm
@INPROCEEDINGS{10.1007/978-3-030-94554-1_23, author={Yi-lin Feng and He-qing Zhang and Cai-ting Peng}, title={Fast Recommendation Method of Personalized Tourism Big Data Information Based on Improved Clustering Algorithm}, proceedings={Advanced Hybrid Information Processing. 5th EAI International Conference, ADHIP 2021, Virtual Event, October 22-24, 2021, Proceedings, Part II}, proceedings_a={ADHIP PART 2}, year={2022}, month={1}, keywords={Digital technology Civil engineering CAD drawing Teaching assistant system}, doi={10.1007/978-3-030-94554-1_23} }- Yi-lin Feng
He-qing Zhang
Cai-ting Peng
Year: 2022
Fast Recommendation Method of Personalized Tourism Big Data Information Based on Improved Clustering Algorithm
ADHIP PART 2
Springer
DOI: 10.1007/978-3-030-94554-1_23
Abstract
The conventional tourism big data information recommendation method does not reduce the search scope of the database, resulting in a long running time of the algorithm. Therefore, based on the improved clustering algorithm, a fast personalized tourism big data information recommendation method is designed. The improved clustering algorithm is designed and the mathematical model of clustering algorithm is established. The semantic similarity of tourism information is calculated. Based on the improved clustering algorithm, the retrieval range of database is reduced, and the classification model of tourist attractions is established, so as to improve the speed of tourism big data information recommendation method. In the experiment of testing the improved clustering algorithm and test information recommendation method, the experimental data of the control group are better than the three control groups. Therefore, the fast recommendation method of personalized tourism big data information based on the improved clustering algorithm is better than the three conventional methods.
