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BiMuF: a bi-directional recommender system with multi-semantic filter for online recruitment

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Abstract

Most existing recommendation research has been concentrated on unidirectional recommendation, i.e., only recommending items to users. However, the platform needs to achieve bi-directional recommendation in many real-world scenarios. For example, in an online recruitment scenario, the recommender system not only needs to recommend positions to candidates, but also recommend candidates to enterprises. In this paper, we develop a new bi-directional recommendation model for online recruitment termed (BiMuF) Bi-directional recommendation with Multi-semantic Filter. In BiMuF, an encoder component is utilized to learn the text embeddings, a multi-semantic filter component is designed to capture important graph representation, and a graph learning component is designed to learn the graph embeddings. In addition, a multi-task learning framework is designed to achieve bi-directional recommendations. In the multi-task learning framework, we share text embeddings and graph embeddings to alleviate the problems of data sparsity, data asymmetry, and feature generalization in online recruitment. Moreover, we conduct three new datasets based on a technology supply and demand docking platform namely South China Technology Commercialization Center, and three new datasets in job recruitment scenario namely Computer Technology-Related Job Recruitment. Extensive experiments in the real-world tasks show that BiMuF outperforms the state-of-the-art methods, verifying the effectiveness of the designs of our model. The code and the latter datasets are available at https://github.com/allminerlab.

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Notes

  1. https://www.sctcc.cn/.

  2. https://baike.baidu.com/.

  3. https://www.paddlepaddle.org.cn.

  4. https://www.sctcc.cn/#/technology_commissioner.

  5. Because there is no baseline designed for employee–employer bi-directional recommendation, we regard users as employees and items as employers in the baselines for experimental purpose.

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Acknowledgements

This work was supported by NSFC (62276277), Guangdong Province Key Laboratory of Computational Science at the Sun Yat-sen University (2020B1212060032), Guangdong Provincial Key Laboratory of Intellectual Property and Big Data (2018B030322016), and Support Scheme of Guangzhou for Leading Talents in Innovation and Entrepreneurship (2019017).

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Authors and Affiliations

  1. School of Information Engineering, Guangdong University of Technology, Panyu, Guangzhou, 510006, Guangdong, China

    Pei-Yuan Lai & Qing-Yun Dai

  2. South China Technology Commercialization Center, Nansha, Guangzhou, 511458, Guangdong, China

    Pei-Yuan Lai & De-Zhang Liao

  3. School of Computer Science and Engineering, Sun Yat-sen University, Panyu, Guangzhou, 510006, Guangdong, China

    Zhe-Rui Yang & Chang-Dong Wang

  4. Guangdong Provincial Key Laboratory of Intellectual Property and Big Data, Tianhe, Guangzhou, 510665, Guangdong, China

    Qing-Yun Dai & Chang-Dong Wang

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  1. Pei-Yuan Lai
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  3. Qing-Yun Dai
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Contributions

P-YL and Z-RY designed the idea, wrote the code and wrote the paper. Q-YD and C-DW provided supervision and reviewed the manuscript. D-ZL prepared the data.

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Correspondence to Chang-Dong Wang.

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Lai, PY., Yang, ZR., Dai, QY. et al. BiMuF: a bi-directional recommender system with multi-semantic filter for online recruitment. Knowl Inf Syst 66, 1751–1776 (2024). https://doi.org/10.1007/s10115-023-01997-1

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