Talent recruitment is a critical, yet costly process for many industries, with high recruitment costs and long hiring cycles. Existing talent recommendation systems increasingly adopt large language models (LLMs) due to their remarkable language understanding capabilities. However, most prior approaches follow a pointwise paradigm, which requires LLMs to repeatedly process some text and fails to capture the relationships among candidates in the list, resulting in higher token consumption and suboptimal recommendations. Besides, LLMs exhibit position bias and the lost-in-the-middle issue when answering multiple-choice questions and processing multiple long documents. To address these issues, we introduce an implicit strategy to utilize LLM's potential output for the recommendation task and propose L3TR, a novel framework for listwise talent recommendation with LLMs. In this framework, we propose a block attention mechanism and a local positional encoding method to enhance inter-document processing and mitigate the position bias and concurrent token bias issue. We also introduce an ID sampling method for resolving the inconsistency between candidate set sizes in the training phase and the inference phase. We design evaluation methods to detect position bias and token bias and training-free debiasing methods. Extensive experiments on two real-world datasets validated the effectiveness of L3TR, showing consistent improvements over existing baselines.

翻译：人才招聘对许多行业而言是一个关键但成本高昂的过程，涉及高额的招聘费用和漫长的招聘周期。现有的人才推荐系统日益采用大语言模型（LLMs），因其卓越的语言理解能力。然而，大多数先前方法遵循逐点范式，这要求LLMs重复处理某些文本，且未能捕捉列表中候选者之间的关联，导致更高的令牌消耗和次优的推荐效果。此外，LLMs在回答多项选择题和处理多个长文档时表现出位置偏差和“迷失在中间”的问题。为解决这些问题，我们引入了一种隐式策略来利用LLM在推荐任务中的潜在输出，并提出了L3TR，一个基于LLMs的列表式人才推荐新框架。在该框架中，我们提出了块注意力机制和局部位置编码方法，以增强文档间处理，缓解位置偏差和并发令牌偏差问题。我们还引入了一种ID采样方法，以解决训练阶段和推理阶段候选集大小不一致的问题。我们设计了检测位置偏差和令牌偏差的评估方法，以及无需训练的去偏方法。在两个真实数据集上的大量实验验证了L3TR的有效性，显示出相对于现有基准方法的持续改进。