A bilateral heterogeneous graph model for interpretable job recommendation considering both reciprocity and competition

doi:10.1007/s42524-023-0280-2

Front. Eng

2024, Vol. 11

Issue (1) : 128-142 https://doi.org/10.1007/s42524-023-0280-2

A bilateral heterogeneous graph model for interpretable job recommendation considering both reciprocity and competition

Xiaowei SHI, Qiang WEI(

), Guoqing CHEN(

)

School of Economics and Management, Tsinghua University, Beijing 100084, China

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Abstract

Amidst the inefficiencies of traditional job-seeking approaches in the recruitment ecosystem, the importance of automated job recommendation systems has been magnified. However, existing models optimized to maximize user clicks for general product recommendations prove inept in addressing the unique challenges of job recommendation, namely reciprocity and competition. Moreover, sparse data on online recruitment platforms can further negatively impact the performance of existing job recommendation algorithms. To counteract these limitations, we propose a bilateral heterogeneous graph-based competition iteration model. This model comprises three integral components: 1) two bilateral heterogeneous graphs for capturing multi-source information from people and jobs and alleviating data sparsity, 2) fusion strategies for synthesizing attributes and preferences to produce mutually beneficial job matches, and 3) a competition-enhancing strategy for dispersing competition realized through a two-stage optimization algorithm. Augmented by granular attention mechanisms for enhanced interpretability, the model’s efficacy, competition dispersion, and interpretability are validated through rigorous empirical evaluations on a real-world recruitment platform.

Keywords job recommendation competition reciprocity interpretability

Corresponding Author(s): Qiang WEI,Guoqing CHEN

Just Accepted Date: 28 December 2023 Online First Date: 05 February 2024 Issue Date: 13 March 2024

Cite this article:

Xiaowei SHI,Qiang WEI,Guoqing CHEN. A bilateral heterogeneous graph model for interpretable job recommendation considering both reciprocity and competition[J]. Front. Eng, 2024, 11(1): 128-142.

URL:

https://academic.hep.com.cn/fem/EN/10.1007/s42524-023-0280-2
https://academic.hep.com.cn/fem/EN/Y2024/V11/I1/128

Fig.1 Bilateral heterogeneous graph competition iteration model.

Tab.1 Summary of attributes

Tab.2 Examples of metapath-based third-order and fourth-order neighbor nodes

Fig.2 Attention-enhanced heterogeneous graph representing learning.

Fig.3 The relationship between HR click score and ranking.

Models	MSE	MAE	Precision	Recall	Accuracy	F1	CTR
ReComJob $Γ c$	0.047	0.133	0.902	0.881	0.881	0.891	0.912
ReComJob $Γ a$	0.042	0.166	0.901	0.880	0.888	0.890	0.898
ReComJob $Γ h$	0.049	0.122	0.900	0.880	0.879	0.890	0.887
MF	0.132	0.236	0.434	0.659	0.659	0.523	0.668
PJFNN	0.057	0.180	0.852	0.807	0.853	0.829	0.862
MUFFIN	0.055	0.151	0.870	0.775	0.868	0.820	0.878
DeepWalk	0.222	0.497	0.510	0.591	0.591	0.548	0.605
Metapath2vec	0.129	0.211	0.565	0.606	0.665	0.585	0.674
HERec	0.072	0.172	0.779	0.705	0.705	0.740	0.715
HGCN	0.060	0.138	0.890	0.814	0.844	0.850	0.853

Tab.3 The overall performance of the baselines and ReComJob model

Fig.4 The impact of hyperparameters.

Tab.4 The effects of different model modules

Fig.5 Job recommendation with intensity comparison.

Fig.6 Accuracy change under various proportions of training sets.

Fig.7 People’s preference path.

Tab.5 An example of job recommendation

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