Study On The Key Technology Of The Employment Recommendation System Of The Migrant Workers

Agricultural information technology is a convenient way to speed up the conversion of agricultural science and technology achievements into productive forces. It is also an important driving force to promote the transformation of the mode of economic development in rural areas and a significant act to accelerate the pace of the new rural construction and the urban-rural planning. As migrant worker is an indispensable part of rural areas, their employment problems have a great effect on the rural economic development as well as the rural stability and harmony. Hence, researching the employment problems of migrant workers has a great practical significance to the society. Due to the special nature of the migrant workers and the rapid degeneration of their work demands, there is still a gap between the research and practical application of the employment recommendation service system of the migrant workers.Considering the technical advantages and disadvantages of the existing recommendation systems, we centered on the key technologies of the personalized employment recommendation system in this paper. Based on a in-depth study of the theory and method of personalized recommendation system, we introduced the model hierarchy notation, the content recommendation algorithm and the collaborative filtering recommendation algorithm to the employment recommendation service model of the migrant workers according to the personalized features of the migrant workers, so that the recommendation system can build better models of the migrant workers’employment intention. Then, according to these models, the system can provide proper jobs to the migrant workers.The main work of the thesis was to research the following key technologies of the employment recommendation system of the migrant workers:1) The establishment of the migrant workers’personalized feature model. Aiming at the diversity of the migrant workers characteristics, we collected the basic information and operation information of the migrant workers through diverse information collection ways and then used the (ID3) algorithm and Support Vector Machine (SVM) to calculate the weight of each characteristic. Furthermore, we introduced the hierarchy notation model to establish the personalized feature model of the migrant workers.2) The improvement of the employment recommendation service algorithm. Based on a detailed analysis of the migrant workers’ personalized feature model and the key technologies of personalized recommendation service, we proposed a combination algorithm for personalized recommendation service combining the content recommendation algorithm with the Item-based collaborative filtering recommendation algorithm. In accordance with the combination recommendation algorithm, the system can realize the personalized employment recommendation service for the migrant workers.3) The implementation of the migrant workers’ employment recommendation service system. Based on the established migrant workers’ feature model and the proposed combination algorithm for personalized recommendation service, we designed and implemented the employment recommendation service system for the migrant workers, which provided a solution to the application of the migrant workers’ employment recommendation service.