Employment recommendation system based on collaborative filtering algorithm

In order to improve the accuracy of the employment recommendation system, a job recommendation model based on the collaborative filtering algorithm was studied. The collaborative filtering algorithm was improved. According to the recruitment preferences of enterprises, the demand of enterprises and the similarity between enterprises and students, a recommendation model was constructed. The job search popularity was calculated using the random walk model. The comprehensive weight was sorted according to the calculation results to complete the recommendation of student jobs. After experimental verification, the model of this study is superior to the classic algorithm in the comparison of evaluation indicators such as accuracy, recall, F1 and MAE. When the number of recommended positions is 10, the accuracy of the algorithm of this study reached 90.2%, which exceeded 80.1% based on the item collaborative filtering algorithm and 71% based on the user collaborative filtering algorithm. The effect of this research model is proved, which provides ideas for solving related problems.