Personalized recommendation algorithm for enterprise science and technology talents based on labeling

Qi Wang

doi:10.1117/12.2668776

28 March 2023 Personalized recommendation algorithm for enterprise science and technology talents based on labeling

Qi Wang

Proceedings Volume 12566, Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022); 125664G (2023) https://doi.org/10.1117/12.2668776
Event: Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022), 2022, Chongqing, China

Abstract

In view of the current enterprise science and technology talent personalized recommendation algorithm in practical application, the number of correctly recommended talents is small. The algorithm recall rate is low. This paper proposes research on enterprise science and technology talent personalized recommendation algorithms based on labeling. In this paper, the four-tuple is used to label the scientific and technological posts and talents in enterprises, and the labels of scientific and technological posts and talents are established. The matrix is used to establish the relationship between scientific and technological talents and posts in enterprises. According to the recruitment behavior of enterprise users and the recruitment behavior of scientific and technological talents, the co-occurrence matrix is used to calculate the correlation between scientific and technological post labels and scientific and technological talents labels. The similarity between scientific and technological talent labels and post labels is calculated from the semantic aspect of labels. By combining the two indicators, the matching degree between the label of scientific and technological talents and the label of scientific and technological posts is determined. The recommendation list is generated, which realizes the personalized recommendation of enterprise scientific and technological talents. Experiments show that the recall rate of the design algorithm is higher than that of the traditional algorithm, which provides effective support for the personalized recommendations of scientific and technological talents in enterprises.

Citation Download Citation

Qi Wang "Personalized recommendation algorithm for enterprise science and technology talents based on labeling", Proc. SPIE 12566, Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022), 125664G (28 March 2023); https://doi.org/10.1117/12.2668776

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KEYWORDS

Technology

Design and modelling

Cooccurrence matrices

Algorithms

Matrices

Semantics

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