JobFormer: Skill-Aware Job Recommendation with Semantic-Enhanced Transformer
arxiv(2024)
摘要
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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