4,397 research outputs found
Entity Personalized Talent Search Models with Tree Interaction Features
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.Comment: This paper has been accepted for publication at ACM WWW 201
Salience and Market-aware Skill Extraction for Job Targeting
At LinkedIn, we want to create economic opportunity for everyone in the
global workforce. To make this happen, LinkedIn offers a reactive Job Search
system, and a proactive Jobs You May Be Interested In (JYMBII) system to match
the best candidates with their dream jobs. One of the most challenging tasks
for developing these systems is to properly extract important skill entities
from job postings and then target members with matched attributes. In this
work, we show that the commonly used text-based \emph{salience and
market-agnostic} skill extraction approach is sub-optimal because it only
considers skill mention and ignores the salient level of a skill and its market
dynamics, i.e., the market supply and demand influence on the importance of
skills. To address the above drawbacks, we present \model, our deployed
\emph{salience and market-aware} skill extraction system. The proposed \model
~shows promising results in improving the online performance of job
recommendation (JYMBII) ( job apply) and skill suggestions for job
posters ( suggestion rejection rate). Lastly, we present case studies to
show interesting insights that contrast traditional skill recognition method
and the proposed \model~from occupation, industry, country, and individual
skill levels. Based on the above promising results, we deployed the \model
~online to extract job targeting skills for all M job postings served at
LinkedIn.Comment: 9 pages, to appear in KDD202
From Query-By-Keyword to Query-By-Example: LinkedIn Talent Search Approach
One key challenge in talent search is to translate complex criteria of a
hiring position into a search query, while it is relatively easy for a searcher
to list examples of suitable candidates for a given position. To improve search
efficiency, we propose the next generation of talent search at LinkedIn, also
referred to as Search By Ideal Candidates. In this system, a searcher provides
one or several ideal candidates as the input to hire for a given position. The
system then generates a query based on the ideal candidates and uses it to
retrieve and rank results. Shifting from the traditional Query-By-Keyword to
this new Query-By-Example system poses a number of challenges: How to generate
a query that best describes the candidates? When moving to a completely
different paradigm, how does one leverage previous product logs to learn
ranking models and/or evaluate the new system with no existing usage logs?
Finally, given the different nature between the two search paradigms, the
ranking features typically used for Query-By-Keyword systems might not be
optimal for Query-By-Example. This paper describes our approach to solving
these challenges. We present experimental results confirming the effectiveness
of the proposed solution, particularly on query building and search ranking
tasks. As of writing this paper, the new system has been available to all
LinkedIn members
The Looming Threat of Fake and LLM-generated LinkedIn Profiles: Challenges and Opportunities for Detection and Prevention
In this paper, we present a novel method for detecting fake and Large
Language Model (LLM)-generated profiles in the LinkedIn Online Social Network
immediately upon registration and before establishing connections. Early fake
profile identification is crucial to maintaining the platform's integrity since
it prevents imposters from acquiring the private and sensitive information of
legitimate users and from gaining an opportunity to increase their credibility
for future phishing and scamming activities. This work uses textual information
provided in LinkedIn profiles and introduces the Section and Subsection Tag
Embedding (SSTE) method to enhance the discriminative characteristics of these
data for distinguishing between legitimate profiles and those created by
imposters manually or by using an LLM. Additionally, the dearth of a large
publicly available LinkedIn dataset motivated us to collect 3600 LinkedIn
profiles for our research. We will release our dataset publicly for research
purposes. This is, to the best of our knowledge, the first large publicly
available LinkedIn dataset for fake LinkedIn account detection. Within our
paradigm, we assess static and contextualized word embeddings, including GloVe,
Flair, BERT, and RoBERTa. We show that the suggested method can distinguish
between legitimate and fake profiles with an accuracy of about 95% across all
word embeddings. In addition, we show that SSTE has a promising accuracy for
identifying LLM-generated profiles, despite the fact that no LLM-generated
profiles were employed during the training phase, and can achieve an accuracy
of approximately 90% when only 20 LLM-generated profiles are added to the
training set. It is a significant finding since the proliferation of several
LLMs in the near future makes it extremely challenging to design a single
system that can identify profiles created with various LLMs.Comment: 33rd ACM Conference on Hypertext and Social Media (HT '23
A Comprehensive Survey of Artificial Intelligence Techniques for Talent Analytics
In today's competitive and fast-evolving business environment, it is a
critical time for organizations to rethink how to make talent-related decisions
in a quantitative manner. Indeed, the recent development of Big Data and
Artificial Intelligence (AI) techniques have revolutionized human resource
management. The availability of large-scale talent and management-related data
provides unparalleled opportunities for business leaders to comprehend
organizational behaviors and gain tangible knowledge from a data science
perspective, which in turn delivers intelligence for real-time decision-making
and effective talent management at work for their organizations. In the last
decade, talent analytics has emerged as a promising field in applied data
science for human resource management, garnering significant attention from AI
communities and inspiring numerous research efforts. To this end, we present an
up-to-date and comprehensive survey on AI technologies used for talent
analytics in the field of human resource management. Specifically, we first
provide the background knowledge of talent analytics and categorize various
pertinent data. Subsequently, we offer a comprehensive taxonomy of relevant
research efforts, categorized based on three distinct application-driven
scenarios: talent management, organization management, and labor market
analysis. In conclusion, we summarize the open challenges and potential
prospects for future research directions in the domain of AI-driven talent
analytics.Comment: 30 pages, 15 figure
Disentangling and Operationalizing AI Fairness at LinkedIn
Operationalizing AI fairness at LinkedIn's scale is challenging not only
because there are multiple mutually incompatible definitions of fairness but
also because determining what is fair depends on the specifics and context of
the product where AI is deployed. Moreover, AI practitioners need clarity on
what fairness expectations need to be addressed at the AI level. In this paper,
we present the evolving AI fairness framework used at LinkedIn to address these
three challenges. The framework disentangles AI fairness by separating out
equal treatment and equitable product expectations. Rather than imposing a
trade-off between these two commonly opposing interpretations of fairness, the
framework provides clear guidelines for operationalizing equal AI treatment
complemented with a product equity strategy. This paper focuses on the equal AI
treatment component of LinkedIn's AI fairness framework, shares the principles
that support it, and illustrates their application through a case study. We
hope this paper will encourage other big tech companies to join us in sharing
their approach to operationalizing AI fairness at scale, so that together we
can keep advancing this constantly evolving field
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