Web-scale search systems typically tackle the scalability challenge with a
two-step paradigm: retrieval and ranking. The retrieval step, also known as
candidate selection, often involves extracting standardized entities, creating
an inverted index, and performing term matching for retrieval. Such traditional
methods require manual and time-consuming development of query models. In this
paper, we discuss applying learning-to-retrieve technology to enhance LinkedIns
job search and recommendation systems. In the realm of promoted jobs, the key
objective is to improve the quality of applicants, thereby delivering value to
recruiter customers. To achieve this, we leverage confirmed hire data to
construct a graph that evaluates a seeker's qualification for a job, and
utilize learned links for retrieval. Our learned model is easy to explain,
debug, and adjust. On the other hand, the focus for organic jobs is to optimize
seeker engagement. We accomplished this by training embeddings for personalized
retrieval, fortified by a set of rules derived from the categorization of
member feedback. In addition to a solution based on a conventional inverted
index, we developed an on-GPU solution capable of supporting both KNN and term
matching efficiently.

应用学习检索技术改进 LinkedIn 的职位搜索和推荐系统，通过构建评估求职者资格的图和利用学习到的链接进行检索，提高应聘人员质量并优化求职者参与度。

学习与职位匹配的检索

Learning to Retrieve for Job Matching

The failure of landing a job for college students could cause serious social
consequences such as drunkenness and suicide. In addition to academic
performance, unconscious biases can become one key obstacle for hunting jobs
for graduating students. Thus, it is necessary to understand these unconscious
biases so that we can help these students at an early stage with more
personalized intervention. In this paper, we develop a framework, i.e., MAYA
(Multi-mAjor emploYment stAtus) to predict students' employment status while
considering biases. The framework consists of four major components. Firstly,
we solve the heterogeneity of student courses by embedding academic performance
into a unified space. Then, we apply a generative adversarial network (GAN) to
overcome the class imbalance problem. Thirdly, we adopt Long Short-Term Memory
(LSTM) with a novel dropout mechanism to comprehensively capture sequential
information among semesters. Finally, we design a bias-based regularization to
capture the job market biases. We conduct extensive experiments on a
large-scale educational dataset and the results demonstrate the effectiveness
of our prediction framework.

本研究提出了 MAYA 预测框架，该框架通过将学术表现嵌入到统一空间中，采用生成对抗网络（GAN）解决了课程异质性问题，采用带有新型 dropout 机制的 LSTM 综合捕捉学期间的顺序信息，并设计基于偏见的正则化来捕捉就业市场的偏见。 在大规模教育数据集上进行了广泛的实验，结果证明了此预测框架的有效性。