Development of the Employment Recommendation System based on K-Means Improved Collaborative Filtering Algorithm

DOI

10.2991/978-94-6463-056-5_72How to use a DOI?

Keywords

K-means clustering algorithm; Collaborative filtering; Employment recommendation system

Abstract

With the massive expansion of higher education, the employment pressure of college graduates has increased dramatically. Meanwhile, graduates are unable to find their preferred positions from the massive and heterogeneous data, and are extremely disturbed by many irrelevant information while searching. To address the above problems, this paper proposes the development of an employment recommendation system based on K-means improved collaborative filtering recommendation algorithm. First, the job seeker and employment job data are collected and preprocessed to understand the characteristics of job seekers and job resources, then the job seeker behavior matrix is established, and similar users are clustered by K-means clustering algorithm, and in the clustering process, the distance between data is calculated by using Euclidean formula, and then the set of neighboring items is selected by improved similarity calculation to predict the job seeker rating and realize recommendation. The experiments show that the method has improved in precision, recall and F-score ratio to some extent.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Pengying Wan
PY  - 2022
DA  - 2022/12/29
TI  - Development of the Employment Recommendation System based on K-Means Improved Collaborative Filtering Algorithm
BT  - Proceedings of the 2022 2nd International Conference on Management Science and Software Engineering (ICMSSE 2022)
PB  - Atlantis Press
SP  - 489
EP  - 494
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-056-5_72
DO  - 10.2991/978-94-6463-056-5_72
ID  - Wan2022
ER  -