A DPC Based Recommendation Algorithm for Internship Positions

In the graduation internship activities, the choice of centralized internship based on school-enterprise cooperation units is limited, and the independent internship method will inevitably lead to the problem of blind job selection due to students' lack of social experience. In this paper, we mine historical practice data and propose a recommendation algorithm based on binomial association rules and density peak clustering analysis named DPC-RA. Through binomial association rules, students' internship results in different units are associated with the classification data of unit names, and the internship unit name data is numerically realized. DPC (density peak clustering) algorithm is used to cluster and mine internship data, and obtain multiple clusters. This method can provide accurate personalized work recommendations and improve the quality of internship. Experiments show that this method is effective.