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Towards Deep and Representation Learning for Talent Search at LinkedIn [article]

Rohan Ramanath, Hakan Inan, Gungor Polatkan, Bo Hu, Qi Guo, Cagri Ozcaglar, Xianren Wu, Krishnaram Kenthapadi, Sahin Cem Geyik
2018 arXiv   pre-print
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.  ...  CONCLUSIONS AND FUTURE WORK In this paper, we presented our experiences of applying deep learning models as well as representation learning approaches for talent search systems at LinkedIn.  ... 
arXiv:1809.06473v1 fatcat:tinmwa5li5bnnksmsn7x6hcmki

Identifying AI talents in LinkedIn database, A machine learning approach

Thomas Roca
2019 Zenodo  
LinkedIn Economic Graph thrives on skills, around 50 thousand of them are listed by LinkedIn and constitute one of the main signals to identify professions or trends.  ...  Searching for keywords in profiles' sections can lead to mis-identification of certain profiles, especially for those related to a field rather than an occupation.  ...  This is particularly interesting for us at LinkedIn, as we would like to identify less specialized members who could be trained and up-skilled to meet to the demand for AI talents.  ... 
doi:10.5281/zenodo.2649208 fatcat:ygh2pfxaunhnxguhqnvmkie5ou

Identifying AI talents among LinkedIn members, A machine learning approach

Thomas Roca
2019 Zenodo  
LinkedIn Economic Graph thrives on skills, around 50 thousand of them are listed by LinkedIn and constitute one of the main signals to identify professions or trends.  ...  Searching for keywords in profiles' sections can lead to mis-identification of certain profiles, especially for those related to a field rather than an occupation.  ...  This is particularly interesting for us at LinkedIn, as we would like to identify less specialized members who could be trained and up-skilled to meet to the demand for AI talents.  ... 
doi:10.5281/zenodo.3240963 fatcat:etngfwtyszh67chl65gnsyee7i

Interview Choice Reveals Your Preference on the Market

Rui Yan, Ran Le, Yang Song, Tao Zhang, Xiangliang Zhang, Dongyan Zhao
2019 Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining - KDD '19  
The key idea is to explore the latent preference given the history of all interviewed candidates for a job posting and the history of all job applications for a particular talent.  ...  To be more specific, we propose a profiling memory module to learn the latent preference representation by interacting with both the job and resume sides.  ...  Researcher from LinkedIn introduced their search architecture with representation learning and sparse entity encoding with deep models [20] .  ... 
doi:10.1145/3292500.3330963 dblp:conf/kdd/YanLSZZ019 fatcat:xpnii72iwzh6bpjco3iamvoeza

Measurement in AI Policy: Opportunities and Challenges [article]

Saurabh Mishra, Jack Clark, C. Raymond Perrault
2020 arXiv   pre-print
This paper surveys problems and opportunities in the measurement of AI systems and their impact, based on a workshop held at Stanford University in the fall of 2019.  ...  We identify six summary challenges inherent to measuring the progress and impact of AI, and summarize over 40 presentations and associated discussions from the workshop.  ...  of co-location between research and industrial capabilities for developing strong deep learning clusters.  ... 
arXiv:2009.09071v1 fatcat:uy6aqaffcbdk3eq2xwt2poax3i

In-Session Personalization for Talent Search [article]

Sahin Cem Geyik, Vijay Dialani, Meng Meng, Ryan Smith
2018 arXiv   pre-print
Previous efforts in recommendation of candidates for talent search followed the general pattern of receiving an initial search criteria and generating a set of candidates utilizing a pre-trained model.  ...  In this paper, we are proposing a candidate recommendation model which takes into account the immediate feedback of the user, and updates the candidate recommendations at each step.  ...  learning within the domain of talent search.  ... 
arXiv:1809.06488v1 fatcat:as5dif4dnrcg3msaahpivttege

Entity Personalized Talent Search Models with Tree Interaction Features

Cagri Ozcaglar, Sahin Geyik, Brian Schmitz, Prakhar Sharma, Alex Shelkovnykov, Yiming Ma, Erik Buchanan
2019 The World Wide Web Conference on - WWW '19  
We also present the offline and online system architecture for the productionization of this hybrid model approach in our Talent Search systems.  ...  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  ...  An exploratory work on utilizing representation and deep learning models for talent search is presented in [23] . ere has also been a shi from querying by keyword to querying by example in talent search  ... 
doi:10.1145/3308558.3313672 dblp:conf/www/OzcaglarGSSSMB19 fatcat:ewxwzuwoxng5ng7d7sib64m6c4

INTEGRATING CULTURAL DIVERSITY IN ORGANIZATIONS: RECRUITMENT AND SELECTION

Ana Carolina Zenone, Bruno Barbosa Cezar, Arnoldo de Hoyos Guevara
2021 Journal on Innovation and Sustainability  
organizational trends in the management of cultural diversity in Brazil and linked mainly to the processes of attraction and selection of talents.  ...  For this purpose, data from a recent survey of 109 Organizations in Brazil, allows to see how the concept management applied in an integral and uniform way in the organizational culture - and permeating  ...  TRENDS IN RECRUITING AND SELECTING SEEKING INCREASING CULTURAL DIVERSITY For the purpose of this study, it may be needed to first show that the search for increasingly diverse talents within the structure  ... 
doi:10.23925/2179-3565.2021v12i2p83-90 fatcat:qqj7wgiiyvhupkinn5alzmqdza

Towards Deep Learning Prospects: Insights for Social Media Analytics

Malik Khizar Hayat, Ali Daud, Abdulrahman A. Alshdadi, Ameen Banjar, Rabeeh Ayaz Abbasi, Yukun Bao, Hussain Dawood
2019 IEEE Access  
INDEX TERMS Social media data, dynamic network, deep learning, feature learning. 36958 He has published about 70 papers in reputed international Impact Factor journals and conferences.  ...  Deep learning (DL) has attracted increasing attention on account of its significant processing power in tasks, such as speech, image, or text processing.  ...  The deep CNN is used to learn discerning features representations automatically, talented of detecting heterogeneous types of clothing images.  ... 
doi:10.1109/access.2019.2905101 fatcat:65mxyey3frdrfngvbfnfss3gpa

Learning Effective Representations for Person-Job Fit by Feature Fusion [article]

Junshu Jiang and Songyun Ye and Wei Wang and Jingran Xu and Xiaosheng Luo
2020 arXiv   pre-print
job post) and then learn features for them.  ...  First, in addition to applying deep learning models for processing the free text in resumes and job posts, which is adopted by existing methods, we extract semantic entities from the whole resume (and  ...  They all apply deep neural networks to learn representations for the resumes and jobs.  ... 
arXiv:2006.07017v1 fatcat:n52ge7nygzdqdblnqoukd5qt2m

How LinkedIn Economic Graph Bonds Information and Product

Xi Chen, Yiqun Liu, Liang Zhang, Krishnaram Kenthapadi
2018 Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining - KDD '18  
Given the simultaneous desire for computing robust, reliable insights and for having insights to satisfy as many job seekers as possible, a key challenge is to reliably infer the insights at the company  ...  We compute embeddings for companies by analyzing the LinkedIn members' company transition data using machine learning algorithms, then compute pairwise similarities between companies based on these embeddings  ...  The authors would like to thank all other members of LinkedIn Salary team for their collaboration for deploying our system as part of the product, and Stuart Ambler, Keren Baruch, Kinjal Basu, Rupesh Gupta  ... 
doi:10.1145/3219819.3219921 dblp:conf/kdd/ChenLZK18 fatcat:rco2bjoodfavbmdl3duyrxtapi

What is a Data Scientist? Analysis of core soft and technical competencies in job postings

Carolina Coelho da Silveira, Carla Bonato Marcolin, Matheus Da Silva, Jean Carlos Domingos
2020 Revista Inovação Projetos e Tecnologias  
Among the most important skills for these professionals are Good Communications, Team Player, Problem Solver, Python, English, and SQL.  ...  With this, the Data Scientist has been demanded as a piece of fundamental value for the organization.  ...  To acquire these jobs, we limited the search location to "Brazil", filtered by "Jobs" and searched for the terms "Data Scientist" and "Cientista de Dados" (the Portuguese translation).  ... 
doi:10.5585/iptec.v8i1.17263 fatcat:ffdzgo6xunbyha4asimue6b2pq

Inclusion of Artificial Intelligence in the Recruitment Process in the Indian Corporate Sector

Supriya Pal, Mohammed Amine Chabane
2018 Zenodo  
As the Indian corporate sector is moving towards digitization, one observes an immense potential for the utilization of artificial intelligence in the human resource department, especially in recruitment  ...  The analysis will also be done in order to explore the strength and weakness of the existing corporate sector, to be more precise, the HRD dynamics in accepting AI.  ...  Intelligent machines can automatically search for adequate job seekers by collecting information from social media sites like Facebook, LinkedIn and Twitter, job boards sites or companies' sites and quickly  ... 
doi:10.5281/zenodo.3592693 fatcat:4wt5xqec45hqtiwtgktk76ao6e

Introduction and overview: TVET in the Middle East – issues, concerns and prospects

Rupert Maclean, John Fien
2017 International Journal of Training Research  
These elements point to a wide range of opportunities for MENA's economies to learn from each other and collaborate on talent development.  ...  Thank you to Guy Berger, Sue Duke, Paul Ko and Igor Perisic at LinkedIn for their outstanding collaboration during the research and production of this Executive Briefing. operates the world's largest professional  ... 
doi:10.1080/14480220.2017.1450211 fatcat:c6z7ckzomngnvjsize6pydlyzu

Toward a traceable, explainable, and fairJD/Resume recommendation system [article]

Amine Barrak, Bram Adams, Amal Zouaq
2022 arXiv   pre-print
Finally, given that multiple software components, datasets, ontology, andmachine learning models will be explored, we aim at proposing a fair, ex-plainable, and traceable architecture for a Resume/JD matching  ...  Typically, pre-trained language models use transfer-based machine learning models to be fine-tuned for a specific field.  ...  For example, a basic NLP taxonomy would have concepts such as machine learning, which is a subset of AI, and deep learning, which is a subset of machine learning.  ... 
arXiv:2202.08960v1 fatcat:52mjmfklefhsfh7fhflmkgrime
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