Personalized position recommendation method based on knowledge graph

Abstract

The invention discloses a personalized position recommendation method based on a knowledge graph. The method includes: acquiring job recruitment field data, acquiring the resume information of a job seeker, and normalizing the job recruitment field data; performing knowledge extraction and fusion to form structured job recruitment field knowledge; saving the acquired structured job recruitment field knowledge into a graph database to build the job recruitment field knowledge graph; building a personalized position recommendation model based on the knowledge graph; reading the resume information of the job seeker, and mapping some attributes in the resume information according to the knowledge graph; according to the knowledge graph, using the position recommendation model to filter positions according to industry categories to form a to-be-recommended position list; quantifying corresponding attributes in the resume information and position information according to the to-be-recommended position list; calculating the similarity of the resume information and the position information, screening top N positions with the highest similarity to the resume of the job seeker to generate arecommendation list, and transmitting the recommendation list to the job seeker.

Description

technical field [0001] The invention relates to a personalized job recommendation method based on a knowledge map, which belongs to the field of recommendation systems in data mining. Background technique [0002] The popularity of the Internet and the advent of the era of big data have brought unique advantages and rare opportunities to online recruitment. Online recruitment has become one of the important ways for job seekers to apply for a job, but the explosive growth of job postings on the Internet has caused the problem of information overload, making it difficult for job seekers to quickly find their desired position among the massive recruitment positions. [0003] Traditional search engine technology has played an important role in solving the problem of information overload. In essence, search engines help users filter information and quickly obtain the information they need. However, when different users enter the same keyword, the final search results are the sam...

AI Technical Summary

Problems solved by technology

In the research of personalized recommendation, although the knowledge map has strong semantic processing ability and fast advantages in information retrieval, it can greatly improve the efficiency of search and the effect of recommendation, and has been used in the recommendation of tourism, movies, books, etc., but Few researchers have combined knowledge graphs and job recommendations for research, and there is no job recommendation method that combines knowledge graphs and personalized recommendation technologies.

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