用多模态大模型预测招聘平台申请量，提升精准邀约效果
Forecasting Application Counts in Talent Acquisition Platforms: Harnessing Multimodal Signals using LMs
- 融合职位信息多模态数据的编码器架构
- 在真实数据上超越现有最先进方法
- 适合招聘系统优化与智能运营团队
随着招聘竞争加剧，招聘机构日益依赖机器学习优化日常运营。然而现有方法多集中于候选人匹配、岗位-技能匹配、岗位分类等任务。本文提出招聘领域新任务：申请量预测，旨在支持高效招募活动设计。发现传统自回归时间序列方法在此任务表现不佳。为此，提出基于大语言模型的多模态融合框架，通过简单编码器整合职位发布元数据的多种模态。在CareerBuilder LLC提供的大规模真实数据集上实验表明，该方法显著优于现有最优方法。
原文摘要 · Abstract (English)
As recruitment and talent acquisition have become more and more competitive, recruitment firms have become more sophisticated in using machine learning (ML) methodologies for optimizing their day to day activities. But, most of published ML based methodologies in this area have been limited to the tasks like candidate matching, job to skill matching, job classification and normalization. In this work, we discuss a novel task in the recruitment domain, namely, application count forecasting, motivation of which comes from designing of effective outreach activities to attract qualified applicants. We show that existing auto-regressive based time series forecasting methods perform poorly for this task. Henceforth, we propose a multimodal LM-based model which fuses job-posting metadata of various modalities through a simple encoder. Experiments from large real-life datasets from CareerBuilder LLC show the effectiveness of the proposed method over existing state-of-the-art methods.
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