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.

翻译：工作推荐旨在为潜在人才提供与其职业轨迹相符的职位描述（JD），这在主动人才招聘中具有关键作用。在实际管理场景中，可用的JD-用户记录通常包含职位描述、用户画像和点击数据，其中用户画像通常因隐私保护需求被概括为技能分布。尽管现有成熟的推荐方法可直接应用，但考虑到JD本身的信息缺失以及JD与用户画像之间的天然异构鸿沟，有效推荐仍面临挑战。为应对这些挑战，我们提出了一种基于语义增强Transformer的新型技能感知推荐模型，用于解析JD并完成个性化工作推荐。具体而言，我们首先对每个JD的关联项进行建模，然后采用具有局部-全局注意力机制的编码器，从JD元组中更深入地挖掘职位内与职位间的依赖关系。此外，我们采用两阶段学习策略实现技能感知推荐：在召回阶段利用技能分布指导JD表示学习，在排序阶段结合用户画像进行最终预测。通过该方法，我们能够为JD学习嵌入丰富的上下文语义表示，同时技能感知推荐为点击率（CTR）预测提供了有效的JD-用户联合表示。为验证该方法在工作推荐中的优越性能，我们在大规模真实数据集和公开数据集上进行了全面实证分析，证明了其有效性与可解释性。