Large Language Models (LLMs) have revolutionized natural language processing
tasks, demonstrating their exceptional capabilities in various domains.
However, their potential for behavior graph understanding in job
recommendations remains largely unexplored. This paper focuses on unveiling the
capability of large language models in understanding behavior graphs and
leveraging this understanding to enhance recommendations in online recruitment,
including the promotion of out-of-distribution (OOD) application. We present a
novel framework that harnesses the rich contextual information and semantic
representations provided by large language models to analyze behavior graphs
and uncover underlying patterns and relationships. Specifically, we propose a
meta-path prompt constructor that leverages LLM recommender to understand
behavior graphs for the first time and design a corresponding path augmentation
module to alleviate the prompt bias introduced by path-based sequence input. By
leveraging this capability, our framework enables personalized and accurate job
recommendations for individual users. We evaluate the effectiveness of our
approach on a comprehensive dataset and demonstrate its ability to improve the
relevance and quality of recommended quality. This research not only sheds
light on the untapped potential of large language models but also provides
valuable insights for developing advanced recommendation systems in the
recruitment market. The findings contribute to the growing field of natural
language processing and offer practical implications for enhancing job search
experiences.

本研究旨在揭示大型语言模型在理解行为图并利用这种理解增强在线招聘中的推荐能力，包括促进分布外应用程序，为此我们提出了一个新框架，该框架利用大型语言模型提供的丰富上下文信息和语义表示来分析行为图并揭示其中的潜在模式和关系，从而实现个性化和准确的工作推荐，并在全面的数据集上评估了我们的方法的有效性。

在线求职推荐中探究大语言模型在图数据理解中的应用

Exploring Large Language Model for Graph Data Understanding in Online  Job Recommendations

Job titles form a cornerstone of today's human resources (HR) processes.
Within online recruitment, they allow candidates to understand the contents of
a vacancy at a glance, while internal HR departments use them to organize and
structure many of their processes. As job titles are a compact, convenient, and
readily available data source, modeling them with high accuracy can greatly
benefit many HR tech applications. In this paper, we propose a neural
representation model for job titles, by augmenting a pre-trained language model
with co-occurrence information from skill labels extracted from vacancies. Our
JobBERT method leads to considerable improvements compared to using generic
sentence encoders, for the task of job title normalization, for which we
release a new evaluation benchmark.

通过将预训练语言模型与从招聘信息中提取的技能标签协同出现信息相结合，我们提出了一种职位称号的神经表示模型， JobBERT 方法相比使用通用语句编码器，在职位标题规范化任务中表现出显著提高，并发布了一个新的评估基准。

JobBERT：通过技能了解工作职称

JobBERT: Understanding Job Titles through Skills

The online recruitment matching system has been the core technology and
service platform in CareerBuilder. One of the major challenges in an online
recruitment scenario is to provide good matches between job posts and
candidates using a recommender system on the scale. In this paper, we discussed
the techniques for applying an embedding-based recommender system for the large
scale of job to candidates matching. To learn the comprehensive and effective
embedding for job posts and candidates, we have constructed a fused-embedding
via different levels of representation learning from raw text, semantic
entities and location information. The clusters of fused-embedding of job and
candidates are then used to build and train the Faiss index that supports
runtime approximate nearest neighbor search for candidate retrieval. After the
first stage of candidate retrieval, a second stage reranking model that
utilizes other contextual information was used to generate the final matching
result. Both offline and online evaluation results indicate a significant
improvement of our proposed two-staged embedding-based system in terms of
click-through rate (CTR), quality and normalized discounted accumulated gain
(nDCG), compared to those obtained from our baseline system. We further
described the deployment of the system that supports the million-scale job and
candidate matching process at CareerBuilder. The overall improvement of our job
to candidate matching system has demonstrated its feasibility and scalability
at a major online recruitment site.

本文研究基于嵌入的推荐系统应用于大规模的招聘场景，提出融合不同级别的文本、语义实体和位置信息的嵌入式聚类方法构建 Faiss 索引来支持候选人检索，最终运用二阶段模型完成了在线招聘匹配系统，并取得了显著的成功。