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Aiming at the lack of personalized recommendation method in existing employment application system, a personalized employment recommendation method based on semantic matching of requirements(PERM-SMR) was proposed in the paper.Firstly, the web crawler technology is used to collect the recruitment information and clean the data.Secondly, word2vec method was selected for corpus training to obtain the word vector model.Thirdly, we performed semantic analysis on the demand information of both sides, calculated the weight of the feature words in the text and generated text vector, and then carried out the requirement information matching and generated the recommendation list.Finally, it is verified by experiments that the PERM-SMR is more accurate and effective, and provides better employment guidance for graduates.

Personalized employment recommendation method based on semantic matching of requirements