Talent recommendation based on attentive deep neural network and implicit relationships of resumes

Yang Huang, Duen Ren Liu*, Shin Jye Lee


研究成果: Article同行評審

1 引文 斯高帕斯(Scopus)


Talent recruitment has become a crucial issue for companies since finding suitable candidates from the massive data on potential candidates from online talent platforms is a challenging task. However, extant studies mainly focus on the scalability and inference ability of models, while the dynamic variability of the importance of each feature in different scenarios is barely addressed. Besides, there is a lack of research on how to depict the hidden potential preference of the job which cannot be derived from job requirements. In this paper, we propose a two-stage resume recommendation model based on deep learning and attention mechanisms, especially considering the latent preference information hidden in the hired employee resumes, named the Attentive Implicit Relationship-Aware Neural Network (AIRANN) model. Specifically, a novel mechanism is proposed herein to extract the hidden potential preference of the corresponding job by deriving the implicit relationship between the target resume and hired employees’ resumes. Existing studies have not considered such resume implicit relationships. Moreover, we propose a Feature Co-Attention mechanism to capture the dynamic interactive importance within the non-text features of both resumes and jobs. For different jobs, the suitability of resumes would be valued from different aspects, including resume implicit relationships, as well as textual and non-textual features. Accordingly, an Aspect-attention mechanism is designed herein to automatically adjust the variant importance of each aspect. Finally, extensive experiments are conducted on a real-world company dataset. The experiment results of ablation studies demonstrate the effectiveness of each mechanism in the proposed AIRANN model. The experiment results also show that the proposed AIRANN model outperforms other baseline methods, showing a general improvement of 13.31%, 12.49%, 6.5% and 7.17% over the state-of-the-art baseline under F1@6, F1@15, NDCG@6 and NDCG@15, respectively.

期刊Information Processing and Management
出版狀態Published - 7月 2023


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