Label big data-based talent recommendation algorithm under system framework and use method thereof

Abstract

The invention discloses a label big data-based talent recommendation algorithm under a system framework, which comprises the following steps: step 1, establishing a multiple regression mathematical model based on statistical analysis of data; step 2, establishing an expected multi-dimensional data table B containing multiple factors, deriving a distribution condition of a candidate content and achievement index IB, a corresponding weight WB and a random error space vector, and taking the distribution condition as a quantifiable index reference for evaluating and judging the candidate content and achievement; step 3, performing deep mining on behavior keyword data, and performing denoising operation on keyword data without behavior factors; and step 4, updating and constructing a multi-dimensional comprehensive evaluation table S, and analyzing variables by taking data in the basic table as metadata and quantitative dimensions, namely attributes, contents, achievements and behaviors, of the basic table as factors. Login simulation is realized by adopting a simulation technical means, an expected effect hotspot distribution series mathematical model is established through key discrete data such as attributes, contents, achievements and behaviors of candidates, and the mathematical model is abstracted into an algorithm.

Description

technical field [0001] The invention relates to the technical field of the Internet, and specifically relates to a tag-based big data talent recommendation algorithm under the system framework and a method for using the same. Background technique [0002] The Internet industry ushered in rapid development from 2016 to 2018, and the demand for talents increased exponentially. In 2020, the Internet talent demand index is as high as 2.12. Among them, the demand for technical posts is the highest, accounting for 24.34% of the demand for talents, and 8.38% for operational posts. The contradiction between the talent gap and the matching of personnel and posts has become increasingly prominent. [0003] The existing technology mainly completes talent recommendation based on the content and attributes of technical personnel's resume, and there are problems in the recommendation results that the recommender is too subjective, the quality is not high, and the efficiency is too low. ...

AI Technical Summary

Problems solved by technology

There are problems of basic data diversity, basic data increment and weighting coefficient setting standards

Images