Title: The construction of college students' job recommendation model based on improved k-means-CF

Authors: Ping Ouyang

Addresses: School of Economics and Management, Foshan Polytechnic, Foshan, 528137, China

Abstract: Based on the internet of things (IoT) technology, building a digital management platform for employment and entrepreneurship service system, recommending suitable corporate positions for students and promoting students' employment and entrepreneurship have become an important issue for each university. At present, the recommendation accuracy and recommendation efficiency of most digital management platforms of employment and entrepreneurship service system are not ideal and not very practical. To this end, the research is based on the idea of data mining, combining collaborative filtering (CF) algorithm, k-means algorithm and dichotomous k-means algorithm to build a personalised recommendation model for graduate jobs, and improve and optimise the career recommendation of the digital management platform of employment and entrepreneurship service system based on this model. The experimental results show that the accuracy rate of model 4 reaches 99.78%, which is significantly higher than the other three models. Therefore, the personalised recommendation model constructed by the study can efficiently and accurately provide students with employment and entrepreneurship information, thus promoting students' employment and entrepreneurship and providing some relief to the huge employment pressure in the current society.

Keywords: internet of things; IoT; employment entrepreneurship; k-means algorithm; collaborative filtering algorithm; dichotomous k-means algorithm; data mining.

DOI: 10.1504/IJCSYSE.2023.132918

International Journal of Computational Systems Engineering, 2023 Vol.7 No.2/3/4, pp.190 - 198

Received: 30 Nov 2022
Accepted: 13 Mar 2023
Published online: 16 Aug 2023 *

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