Vacant position intelligent recommendation method based on GPU (graphics processing unit) acceleration

Abstract

The invention provides a vacant position intelligent recommendation method based on GPU (graphics processing unit) acceleration. The vacant position intelligent recommendation method is applied in a recommendation engine on a recruitment website. With the adoption of the vacant position intelligent recommendation method, registration information and visit information of an applicant are expressed into matrix form suitable for being processed by the GPU; and with the adoption of strong processing ability of the GPU and a computing mode of remedying shortage of the GPU property by high bandwidth, thousands of GPU threads are subjected to concurrent computing. With the adoption of the vacant position intelligent recommendation method, the level of similarity between two applicants is calculated based on an Euclidean distance formula, then the appropriate level of all vacant positions corresponding to the applicant is predicated intelligently according to the attribute information of the applicant, and finally the most suitable vacant position is recommended to the applicant. The expensive traditional computer cluster technology is not used when mass data are processed but a GPU technique with high cost performance is used, so that the ultimate aims of high performance, low energy consumption and low cost are realized.

Description

technical field [0001] The present invention generally relates to the field of computer recommendation engines, and in particular to an intelligent recommendation method for recruitment positions based on GPU acceleration. Background technique [0002] Online recruitment and online job hunting have become a major way of talent exchange in today's society. The employer and the applicant register an account on the third-party talent recruitment website, search and communicate with each other, in order to find the most satisfactory object. Talent recruitment websites have a large number of positions. After logging into the website, an applicant can only browse positions from the job list, or rely on keywords to search for positions that may be suitable for him. The number of job listings is often huge, and applicants cannot find the jobs they are interested in in a short period of time; while using keyword search, the number and accuracy of keywords used by applicants are very...

AI Technical Summary

Problems solved by technology

This recommendation method has the following disadvantages: (1) The positions recommended for each applicant are the same and lack of personalization; (2) Those positions that are not popular but very suitable for applicants cannot be recommended; ( 3) Traditional calculations only use the CPU for numerical calculations, and the calculation performance is extremely low in the face of a large number of applicants and job data

[0004] In terms of improving the computing performance of data mining, many enterprises and research institutes use computer clusters to improve computing speed by using the concurrency of clusters. However, based on cost considerations, many units cannot deploy expensive computer clusters.

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