LinkedIn skills

Bastian, Mathieu; Hayes, Matthew; Vaughan, William; Shah, Sam; Skomoroch, Peter; Kim, Hyungjin; Uryasev, Sal; Lloyd, Christopher

doi:10.1145/2645710.2645729

Cited by 53 publications

(3 citation statements)

References 10 publications

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“…The feasibility of large-scale studies of SM users has been demonstrated for a variety of text-based SM platforms, such as LinkedIn (Bastian et al, 2014), Facebook (Heidenreich et al, 2020) or Twitter (Giachanou & Crestani, 2016). Widespread methods for the analysis of such data include topic modelling (Calderón et al, 2020) and sentiment analysis (Giachanou & Crestani, 2016).…”

Section: Background: Analysis Of Migrants Using Smmentioning

confidence: 99%

The migrant perspective: Measuring migrants' movements and interests using geolocated tweets

Mast,

Sapena,

Mühlbauer

et al. 2023

Population Space and Place

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Geolocated social media data hold a hitherto untapped potential for exploring the relationship between user mobility and their interests at a large scale. Using geolocated Twitter data from Nigeria, we provide a feasibility study that demonstrates how the linkage of (1) a trajectory analysis of Twitter users' geolocation and (2) natural language processing of Twitter users' text content can reveal information about the interests of migrants. After identifying migrants via a trajectory analysis, we train a language model to automatically detect the topics of the migrants' tweets. Biases of manual labelling are circumvented by learning community‐defined topics from a Nigerian web forum. Results suggest that differences in users' mobility correlate with varying interests in several topics, most notably religion. We find that Twitter data can be a flexible source for exploring the link between users' mobility and interests in large‐scale analyses of urban populations. The joint use of spatial techniques and text analysis enables migration researchers to (a) study migrant perspectives in greater detail than is possible with census data and (b) at a larger scale than is feasible with interviews. Thereby, it provides a valuable complement to interviews, surveys and censuses, and holds a large potential for further research.

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Section: Background: Analysis Of Migrants Using Smmentioning

confidence: 99%

The migrant perspective: Measuring migrants' movements and interests using geolocated tweets

Mast,

Sapena,

Mühlbauer

et al. 2023

Population Space and Place

View full text Add to dashboard Cite

show abstract

“…Past editions of the RecSys conference have seen a steady number of research contributions on automating and (more commonly) supporting job recommendation [3,7,9,11,13,17,19], all of which have focused on the core HR task of recruitment through the development of automatic job recommendation algorithms. In addition to this research, the RecSys Challenges of 2016 [1] and 2017 [2] both focused on the task of job recommendation, with Xing, a social network for businesses mainly operating in German-speaking countries, providing the training data.…”

Section: Relevance For Recsysmentioning

confidence: 99%

Second Workshop on Recommender Systems for Human Resources (RecSys in HR 2022)

Bogers

Graus²,

Kaya

et al. 2022

Proceedings of the 16th ACM Conference on Recommender Systems

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“…The LinkedIn Skills system (Bastian et al 2014) uses a data-driven approach to build a skills folksonomy. The folksonomy-building pipeline consists of discovery, disambiguation, and deduplication steps.…”

Section: Large-scale Occupational Skills Normalization For Online Rec...mentioning

confidence: 99%

Large‐Scale Occupational Skills Normalization for Online Recruitment

Hoang¹,

Mahoney²,

Javed³

et al. 2018

AI Magazine

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Job openings often go unfulfilled despite a surfeit of unemployed or underemployed workers. One of the main reasons for this is a mismatch between the skills required by employers and the skills that workers possess. This mismatch, also known as the skills gap, can pose socioeconomic challenges for an economy. A first step in alleviating the skills gap is to accurately detect skills in human capital data such as resumes and job ads. Comprehensive and accurate detection of skills facilitates analysis of labor market dynamics. It also helps bridge the divide between supply and demand of labor by facilitating reskilling and workforce training programs. In this paper, we describe SKILL, a Named Entity Normalization (NEN) system for occupational skills. SKILL is composed of 1) A skills tagger which uses properties of semantic word vectors to recognize and normalize relevant skills, and 2) A skill entity sense disambiguation component which infers the correct meaning of an identified skill. We discuss the technical design and the synergy between data science and engineering that was required to transform the system from a research prototype to a production service that serves customers from across the organization. We also discuss establishing customer feedback loops, and it led to improvements to the system over time. SKILL is currently used by various internal teams at CareerBuilder for big data workforce analytics, semantic search, job matching, and recommendations.

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LinkedIn skills

Cited by 53 publications

References 10 publications

The migrant perspective: Measuring migrants' movements and interests using geolocated tweets

The migrant perspective: Measuring migrants' movements and interests using geolocated tweets

Second Workshop on Recommender Systems for Human Resources (RecSys in HR 2022)

Large‐Scale Occupational Skills Normalization for Online Recruitment

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