Job recommendation method based on improved Canopy clustering collaborative filtering algorithm

Abstract

The invention discloses a job recommendation method based on an improved Canopy clustering collaborative filtering algorithm. The method comprises the following steps of 1, obtaining job application information data of a user; step 2, taking the job hunting information data of the user as a data processing object, and clustering the user job hunting information data to obtain a plurality of Canopyclasses through the improved Canopy clustering collaborative filtering; and step 3, carrying out corresponding position recommendation on the users in each Canopy class. The recruitment information activeness and score of the user are introduced into calculation through the improved Canopy clustering collaborative filtering algorithm to obtain the recognition degree of the user for the recruitment information; the job information of the user is clustered through the recognition degree, then job recommendation is carried out, the requirement of the user for interests in multiple fields is met,corresponding recommendation is carried out on the user, and the accuracy of job recommendation is improved.

Description

technical field [0001] The present invention relates to the field of computers. More specifically, it relates to a job recommendation method based on the improved Canopy clustering collaborative filtering algorithm. Background technique [0002] With the rapid development of the Internet and the advent of the era of big data, the efficient processing of big data has become particularly important in all walks of life. In the contemporary era of information overload, a large number of users have begun to apply for jobs, purchase, socialize, etc. on the Internet. Users need to efficiently and quickly obtain useful information from network big data, which requires corresponding Internet data analysis to meet user requirements. In order to obtain more users and the development of enterprises, big data processing methods are also constantly Improved updates. [0003] Based on the characteristics of multiple resources and a large amount of information on the Internet, online job ...

AI Technical Summary

Problems solved by technology

The recommendation system in the online recruitment platform establishes the binary relationship between users and information products, uses the data generated by user behavior to mine the objects of interest to each user and recommends them. The existing recommendation methods are: based on keyword recommendation, The accuracy of its recommendation is usually not high. Input recommendations based on standardized formats can no longer meet the diverse needs of current job seekers. Recommendations based on test questions are not accurate enough.

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