Related papers: Deep Job Understanding at LinkedIn

At LinkedIn, we want to create economic opportunity for everyone in the global workforce. A critical aspect of this goal is matching jobs with qualified applicants. To improve hiring efficiency and reduce the need to manually screening each…

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…

A post embedding (representation of text in embedding space that effectively captures semantic meaning) is a foundational component of LinkedIn that is consumed by product surfaces in retrieval and ranking (e.g., ranking posts in the feed…

Online professional social networks such as LinkedIn have enhanced the ability of job seekers to discover and assess career opportunities, and the ability of job providers to discover and assess potential candidates. For most job seekers,…

Large language models (LLMs) have achieved strong performance across a wide range of natural language processing tasks. However, deploying LLMs at scale for domain specific applications, such as job-person fit and explanation in job seeking…

The recently launched LinkedIn Salary product has been designed with the goal of providing compensation insights to the world's professionals and thereby helping them optimize their earning potential. We describe the overall design and…

Social network platforms have increased and become very popular in the last decade; they allow people to create an online account to then interact with others creating a complicated net of connections. LinkedIn is one of the most used…

LinkedIn Talent Solutions business contributes to around 65% of LinkedIn's annual revenue, and provides tools for job providers to reach out to potential candidates and for job seekers to find suitable career opportunities. LinkedIn's job…

LinkedIn is the largest professional network with more than 350 million members. As the member base increases, searching for experts becomes more and more challenging. In this paper, we propose an approach to address the problem of…

With the increasing amount of information on the Internet, recommender systems are becoming increasingly crucial in supporting people to find and explore relevant content. This is also true in the online recruitment space, with websites…

In August 2019, we introduced to our members and customers the idea of moving LinkedIn's two core talent products -- Jobs and Recruiter -- onto a single platform to help talent professionals be even more productive. This single platform is…

LinkedIn has grown to become a platform hosting diverse sources of information ranging from member profiles, jobs, professional groups, slideshows etc. Given the existence of multiple sources, when a member issues a query like "software…

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…

Semantic search with large language models (LLMs) enables retrieval by meaning rather than keyword overlap, but scaling it requires major inference efficiency advances. We present LinkedIn's LLM-based semantic search framework for AI Job…

The LinkedIn Salary product was launched in late 2016 with the goal of providing insights on compensation distribution to job seekers, so that they can make more informed decisions when discovering and assessing career opportunities. The…

Online recruitment platforms require recommendation methods capable of retrieving relevant job opportunities from large and heterogeneous collections of job postings. Keyword-based search is efficient and interpretable, but it may fail to…

Many search systems work with large amounts of natural language data, e.g., search queries, user profiles, and documents. Building a successful search system requires a thorough understanding of textual data semantics, where deep learning…

We present LiRank, a large-scale ranking framework at LinkedIn that brings to production state-of-the-art modeling architectures and optimization methods. We unveil several modeling improvements, including Residual DCN, which adds attention…

LinkedIn search is deeply personalized - for the same queries, different searchers expect completely different results. This paper presents our approach to achieving this by mining various data sources available in LinkedIn to infer…

Recent years have brought significant advances to Natural Language Processing (NLP), which enabled fast progress in the field of computational job market analysis. Core tasks in this application domain are skill extraction and…