JobFormer: Skill-Aware Job Recommendation with Semantic-Enhanced Transformer
Authors: 
Zhihao Guan, Jia-Qi Yang, Yang Yang, Hengshu Zhu, Wenjie Li, Hui XiongAuthors Info & Claims
Zhihao Guan, Jia-Qi Yang, Yang Yang, Hengshu Zhu, Wenjie Li, Hui XiongAuthors Info & ClaimsArticle No.: 18, Pages 1 - 20
Abstract
Job recommendation aims to provide potential talents with suitable job descriptions (JDs) consistent with their career trajectory, which plays an essential role in proactive talent recruitment. In real-world management scenarios, the available JD-user records always consist of JDs, user profiles, and click data, in which the user profiles are typically summarized as the user's skill distribution for privacy reasons. Although existing sophisticated recommendation methods can be directly employed, effective recommendation still has challenges considering the information deficit of JD itself and the natural heterogeneous gap between JD and user profile. To address these challenges, we proposed a novel skill-aware recommendation model based on the designed semantic-enhanced Transformer to parse JDs and complete personalized job recommendation. Specifically, we first model the relative items of each JD and then adopt an encoder with the local-global attention mechanism to better mine the intra-job and inter-job dependencies from JD tuples. Moreover, we adopt a two-stage learning strategy for skill-aware recommendation, in which we utilize the skill distribution to guide JD representation learning in the recall stage and then combine the user profiles for final prediction in the ranking stage. Consequently, we can embed rich contextual semantic representations for learning JDs, while skill-aware recommendation provides effective JD-user joint representation for click-through rate (CTR) prediction. To validate the superior performance of our method for job recommendation, we present a thorough empirical analysis of large-scale real-world and public datasets to demonstrate its effectiveness and interpretability.
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Index Terms
- JobFormer: Skill-Aware Job Recommendation with Semantic-Enhanced Transformer
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Publication History
Published: 28 December 2024
Online AM: 26 October 2024
Accepted: 21 October 2024
Revised: 21 July 2024
Received: 17 October 2023
Published in TKDD Volume 19, Issue 1
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- Natural Science Foundation of Jiangsu Province of China under Grant
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