Talent Search systems aim to recommend potential candidates who are a good
match to the hiring needs of a recruiter expressed in terms of the recruiter's
search query or job posting. Past work in this domain has focused on linear and
nonlinear models which lack preference personalization in the user-level due to
being trained only with globally collected recruiter activity data. In this
paper, we propose an entity-personalized Talent Search model which utilizes a
combination of generalized linear mixed (GLMix) models and gradient boosted
decision tree (GBDT) models, and provides personalized talent recommendations
using nonlinear tree interaction features generated by the GBDT. We also
present the offline and online system architecture for the productionization of
this hybrid model approach in our Talent Search systems. Finally, we provide
offline and online experiment results benchmarking our entity-personalized
model with tree interaction features, which demonstrate significant
improvements in our precision metrics compared to globally trained
non-personalized models.

本文提出了一种基于实体个性化的人才搜索模型， 结合广义线性混合模型和梯度增强决策树模型提供个性化的推荐，并使用 GBDT 生成的非线性交互特征。此外，文章还介绍了离线和在线系统架构，并使用离线和在线实验结果证明了此模型与全局训练的非个性化模型相比在准确率指标上的显着提高。

树交互特征的实体个性化人才搜索模型

Entity Personalized Talent Search Models with Tree Interaction Features

Talent search and recommendation systems at LinkedIn strive to match the
potential candidates to the hiring needs of a recruiter or a hiring manager
expressed in terms of a search query or a job posting. Recent work in this
domain has mainly focused on linear models, which do not take complex
relationships between features into account, as well as ensemble tree models,
which introduce non-linearity but are still insufficient for exploring all the
potential feature interactions, and strictly separate feature generation from
modeling. In this paper, we present the results of our application of deep and
representation learning models on LinkedIn Recruiter. Our key contributions
include: (i) Learning semantic representations of sparse entities within the
talent search domain, such as recruiter ids, candidate ids, and skill entity
ids, for which we utilize neural network models that take advantage of LinkedIn
Economic Graph, and (ii) Deep models for learning recruiter engagement and
candidate response in talent search applications. We also explore learning to
rank approaches applied to deep models, and show the benefits for the talent
search use case. Finally, we present offline and online evaluation results for
LinkedIn talent search and recommendation systems, and discuss potential
challenges along the path to a fully deep model architecture. The challenges
and approaches discussed generalize to any multi-faceted search engine.

本文研究了深度学习在 LinkedIn 人才搜索和推荐系统中的应用，利用神经网络模型学习人才搜索领域中的稀疏实体的语义表示，利用学习排名方法来提升人才搜索、推荐系统的表现。

领英人才搜索的深度与表示学习

Towards Deep and Representation Learning for Talent Search at LinkedIn

One key challenge in talent search is how to translate complex criteria of a
hiring position into a search query. This typically requires deep knowledge on
which skills are typically needed for the position, what are their
alternatives, which companies are likely to have such candidates, etc. However,
listing examples of suitable candidates for a given position is a relatively
easy job. Therefore, in order to help searchers overcome this challenge, we
design a next generation of talent search paradigm at LinkedIn: Search by Ideal
Candidates. This new system only needs the searcher to input one or several
examples of suitable candidates for the position. The system will generate a
query based on the input candidates and then retrieve and rank results based on
the query as well as the input candidates. The query is also shown to the
searcher to make the system transparent and to allow the searcher to interact
with it. As the searcher modifies the initial query and makes it deviate from
the ideal candidates, the search ranking function dynamically adjusts an
refreshes the ranking results balancing between the roles of query and ideal
candidates. As of writing this paper, the new system is being launched to our
customers.

在 LinkedIn 中，我们设计了一种新的人才搜索范例，名为 “理想候选人搜索”，只需要用户输入一到多个合适候选人的示例，系统将基于输入的候选人生成一个查询并检索和排名结果。