解构偏好与资格：基于大语言模型的约束双视角推理 job 推荐
De-conflating Preference and Qualification: Constrained Dual-Perspective Reasoning for Job Recommendation with Large Language Models
AI总结：
JobRec通过约束双视角推理解构偏好与资格，提升职位推荐的可控性和匹配质量。
摘要：
专业职位推荐涉及一个复杂的二部匹配过程，必须协调候选人的主观偏好与雇主的客观资格。尽管大语言模型（LLMs）适合建模简历和职位描述的丰富语义，但现有范式往往将这两个决策维度合并为单一交互信号，在招聘漏斗筛选下导致监督混淆，并限制了政策可控性。为解决这些挑战，我们提出了JobRec，一种生成式职位推荐框架，通过约束双视角推理来解构偏好与资格。JobRec引入了一个统一的语义对齐方案，将候选人的职位属性对齐到结构化的语义层，并采用两阶段协作训练策略，学习解耦专家以分别推断偏好与资格。基于这些专家，一个基于拉格朗日的政策对齐模块在显式资格要求下优化推荐，使可控的权衡成为可能。为缓解数据稀缺，我们构建了一个由专家优化的合成数据集。实验表明，JobRec在强基线模型上表现一致，并为策略感知的专业匹配提供了改进的可控性。
英文摘要：
Professional job recommendation involves a complex bipartite matching process that must reconcile a candidate's subjective preference with an employer's objective qualification. While Large Language Models (LLMs) are well-suited for modeling the rich semantics of resumes and job descriptions, existing paradigms often collapse these two decision dimensions into a single interaction signal, yielding confounded supervision under recruitment-funnel censoring and limiting policy controllability. To address these challenges, We propose JobRec, a generative job recommendation framework for de-conflating preference and qualification via constrained dual-perspective reasoning. JobRec introduces a Unified Semantic Alignment Schema that aligns candidate and job attributes into structured semantic layers, and a Two-Stage Cooperative Training Strategy that learns decoupled experts to separately infer preference and qualification. Building on these experts, a Lagrangian-based Policy Alignment module optimizes recommendations under explicit eligibility requirements, enabling controllable trade-offs. To mitigate data scarcity, we construct a synthetic dataset refined by experts. Experiments show that JobRec consistently outperforms strong baselines and provides improved controllability for strategy-aware professional matching.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
