Research Article
Research on the Application of User Interest Model and Apriori Algorithm in College Students’ Education Recommendation
@INPROCEEDINGS{10.1007/978-3-030-87900-6_23, author={Peng Zhang}, title={Research on the Application of User Interest Model and Apriori Algorithm in College Students’ Education Recommendation}, proceedings={Application of Big Data, Blockchain, and Internet of Things for Education Informatization. First EAI International Conference, BigIoT-EDU 2021, Virtual Event, August 1--3, 2021, Proceedings, Part I}, proceedings_a={BIGIOT-EDU}, year={2021}, month={10}, keywords={User interest model Apriori algorithm College students employment recommendation Big data optimization fusion processing Feature point matching Adaptive matching}, doi={10.1007/978-3-030-87900-6_23} }- Peng Zhang
Year: 2021
Research on the Application of User Interest Model and Apriori Algorithm in College Students’ Education Recommendation
BIGIOT-EDU
Springer
DOI: 10.1007/978-3-030-87900-6_23
Abstract
In order to realize the intelligent recommendation and interest matching of College Students’ employment, a college students’ employment recommendation model based on user interest model and Apriori algorithm is proposed. This paper constructs the user interest information collection and big data distribution model of College Students’ employment, uses the big data association information mining method to match the interest features of College Students’ employment, and constructs the interest correlation feature quantity of College Students’ Employment under the control of association rules, so as to optimize and fuse the interest feature big data of College Students’ employment recommendation. Apriori algorithm is used to adaptively match the interest feature points of College Students’ employment recommendation, and fuzzy adaptive optimization method is used to optimize the recommendation of College Students’ employment behavior. The simulation results show that the reliability of this method is good, and the employment satisfaction level of college students is improved.
