Application of Internet of Things technology in Human resource recommendation engine

Abstract

In order to avoid the phenomenon of information overload in the field of human resources, the author proposes a human resource recommendation engine based on Internet of Things technology. Firstly, the application fields of the Internet of Things are introduced, and the human resource recommendation engine is designed, use a mixed referral approach in the HR field. We chose a hybrid recommendation strategy of PLSA and content based on the Internet of Things. The author introduces the human resource recommendation and content-based human resource recommendation of PLSA based on the Internet of Things respectively, on the basis of the above introduction, through the analysis of the existing hybrid recommendation strategy, a weighted hybrid method of PLSA based on the Internet of Things and content-based recommendation algorithm is proposed. In order to verify the feasibility of the proposed IoT-based PLSA and content-based HR recommendation method, we conducted a multi-step experiment. We selected 50 graduating students from A University to participate in this experiment, and the results show that in order to verify the overall effect of the algorithm, with accuracy and recall as evaluation criteria, we conducted experiments on recommendation algorithms of PLSA based on Internet of Things, content-based recommendation algorithms, and recommendation algorithms based on PLSA and content weighting mixture, where, the number of z in PLSA algorithm is 10, the value of β in mixed recommendation is 0.6, the size of N in TopN recommendation is N=5,10,20, and 30. Experiments were carried out on the accuracy and recall of the three recommendation algorithms, the number of z in PLSA algorithm is 10, the value of β is 0.6, and the size of N in TopN recommendation is N=3,5,7,9. The recommendation effect of PLSA based on the Internet of Things is better than that of content-based recommendation algorithm, and the recommendation effect of the combination of the two algorithms is better than either of them.