Research on the Strategy of Improving College Students’ Career Adaptability in Application-oriented Colleges and Universities

Application-oriented colleges and universities have become the main force in the popularization of higher education in China, and their school running orientation is to cultivate application-oriented talents to adapt to social development. Career adaptability is a hot topic in the field of career psychology abroad. Therefore, it is of great significance to carry out the education of improving college students’ career adaptability in Application-oriented Colleges and universities, summarize college students’ career adaptability, analyze the necessity of improving college students’ career adaptability at this stage, and put forward the strategies of improving college students’ career adaptability in Application-oriented Colleges and universities, so as to enable students to achieve real job matching in the process of employment.

Emerging Dynamics in Tourism Industry Workforce Mobility in Southern Cross River State, Nigeria

The challenge of brain drain/workforce mobility/out-migration in tourism and hospitality industry workforce is a rising phenomenon. The study investigated the factors responsible for the emerging dynamics in tourism industry workforce mobility in Southern Cross River, Nigeria. Data was generated through the administration of structured questionnaire and oral interviews on the staff and workforce of tourism industry. The results revealed that on aggregate, a total of 63.50% workers had intentions to migrate from their job while 36.50% workers had no intention to migrate from their current job. Further findings revealed that low remuneration, unfavourable working hours, stress, lack of career prospects, lack of job security, unsatisfactory working conditions, health risk, and challenge of job matching were among the reasons for job out-migration among the industry workforce. The study recommended that both public and private sectors should create enabling tourism work-environment for retention of tourism employees. Received: 14 August 2021 / Accepted: 2 October 2021 / Published: 5 November 2021

Design and Simulation of Human Resource Allocation Model Based on Double-Cycle Neural Network

The rationalization of human resource management is helpful for enterprises to efficiently train talents in the field, improve the management mode, and increase the overall resource utilization rate of enterprises. The current computational models applied in the field of human resources are usually based on statistical computation, which can no longer meet the processing needs of massive data and do not take into account the hidden characteristics of data, which can easily lead to the problem of information scarcity. The paper combines recurrent convolutional neural network and traditional human resource allocation algorithm and designs a double recurrent neural network job matching recommendation algorithm applicable to the human resource field, which can improve the traditional algorithm data training quality problem. In the experimental part of the algorithm, the arithmetic F1 value in the paper is 0.823, which is 20.1% and 7.4% higher than the other two algorithms, respectively, indicating that the algorithm can improve the hidden layer features of the data and then improve the training quality of the data and improve the job matching and recommendation accuracy.

A Deep-Learning-Inspired Person-Job Matching Model Based on Sentence Vectors and Subject-Term Graphs

In this study, an end-to-end person-to-job post data matching model is constructed, and the experiments for matching people with the actual recruitment data are conducted. First, the representation of the constructed knowledge in the low-dimensional space is described. Then, it is explained in the Bidirectional Encoder Representations from Transformers (BERT) pretraining language model, which is introduced as the encoding model for textual information. The structure of the person-post matching model is explained in terms of the attention mechanism and its computational layers. Finally, the experiments based on the person-post matching model are compared with a variety of person-post matching methods in the actual recruitment dataset, and the experimental results are analyzed.

Structural change in the job matching process in the United States, 1923–1932

This paper explores the structural change in the job matching process in the United States from 1923 to 1932. Bai–Perron test is employed with data from public employment offices. The result shows that there are two structural changes, July 1928 and August 1930. The first breakpoint corresponds to approximately a year before the stock market crash, implying that there was a sign of downturn in the labor market when the economy was still in a period of expansion. The second break point is related to the Great Contraction when the entire economy was going in a downward spiral.