Welcome to The AAAI Workshop on Computational Jobs Marketplace to be held as part of The 39th Annual AAAI Conference on Artificial Intelligence.

Online job marketplaces such as Indeed.com, ZipRecruiter, CareerBuilder and LinkedIn Inc. help millions of job seekers find their next job. These platforms also provide services for thousands of employers to fill their opening positions. With all players in the ecosystem, the market size of this industry is projected to steadily grow and reach $43 billion dollars in 2027. On top of that, the global pandemic COVID-19 in 2020 and the emerging AI trends have profoundly transformed workplaces and the online jobs marketplace, creating and driving new types of jobs and marketplace technologies around the world. Today, online job marketplaces play a central role in this new wave of digital revolution of workforce and workplaces. While this industry generates tremendous growth in the past several years, technological innovations around this industry have yet to come. Many technologies, such as search systems, recommender systems as well as advertising systems that the industry heavily relies on are deeply rooted in their more generic counterparts, which may not address the unique challenges in this industry for better products serving both job seekers and employers/recruiters.

This workshop would play a critical role to bring together the research and development community in this industry especially around data science and machine learning and facilitate innovations on theories, models, systems and practices in this currently scattered community. An expected outcome of the workshop is to create awareness of this emerging industry with its technological opportunities and challenges, which might foster future research and development, creating novel products to serve future job seekers and employers/recruiters.

KEYNOTE SPEAKERS

daniel

Daniel Hewlett, Principal Staff AI Engineer at LinkedIn

Talk Title: TBD

hasan

Mohammad Al Hasan, Professor of Computer Science at Indianna University

Talk Title: TBD

PROGRAM

Time Content
8:55 AM – 9:00 AM Opening Remarks from Chairs
9:00 AM – 9:45 AM Keynote Talk: Daniel Hewlett from LinkedIn
9:45 AM – 9:55 AM Talk: Trading off Relevance and Revenue in the Jobs Marketplace: Estimation, Optimization and Auction Design [PDF]
Farzad Pourbabaee (LinkedIn), Sophie Yanying Sheng (LinkedIn), Peter B. McCrory (LinkedIn), Luke Simon (LinkedIn), Di Mo (LinkedIn)
9:55 AM – 10:05 AM Talk: Mitigating Language Bias in Cross-Lingual Job Retrieval: A Recruitment Platform Perspective [PDF]
Napat Laosaengpha (Chulalongkorn University), Thanit Tativannarat (Chulalongkorn University), Attapol Rutherford (Chulalongkorn University), Ekapol Chuangsuwanich (Chulalongkorn University)
10:05 AM – 10:15 AM Talk: Multi-objective ranking for job marketplace optimization
Rong Liu
10:15 AM – 10:25 AM Talk: Enterprise Experimentation with Hierarchical Entities [PDF]
Shan Ba (LinkedIn), Shilpa Garg (LinkedIn), Jitendra Agarwal (LinkedIn), Hanyue Zhao (LinkedIn)
10:25 AM – 10:35 AM Talk: Lessons Learned — Building ML Models to Remove Irrelevant Results in Job Search
Gabriel Womark, Ritvik Kharkar, Ishan Shrivastava
10:35 AM – 11:20 AM Keynote Talk: Mohammad Al Hasan from Indiana University
11:20 AM - 11:30 AM Talk: Weak Supervision For Improved Precision In Search Systems
Sriram Vasudevan
11:30 AM – 11:40 AM Talk: Harnessing Large Language Models for Cost-Effective Relevance Labeling in Job Search Systems
Ishan Shrivastava, Nadav Barkai, Ritvik Kharkar
11:40 AM – 11:50 AM Talk: Migrating a Job Search Relevance Function
Bennett Mountain, Gabriel Womark, Ritvik Kharkar
11:50 AM – 12:00 PM Talk: Ordinal Regression for Job Search Keyword Similarity Prediction
Md Ahsanul Kabir, Kareem Abdelfatah, Mohammed Korayem, Mohammad Hasan
12:05 PM – 12:10 PM Closing Remarks from Chairs

ORGANIZERS

Liangjie

Liangjie Hong is a Director of Engineering, AI at LinkedIn Inc., managing teams of machine learning engineers and applied researchers to drive AI solutions for Talent Solutions, a core LinkedIn business that connects job seekers and recruiters in a two-sided marketplace. Before that, he was a Director of Engineering at Etsy Inc., leading the overall data science and machine learning efforts across search, recommendation and advertising. Previously, he was Senior Manager of Research at Yahoo Research, leading science efforts for Personalization and Search Sciences. Prior to Yahoo Research, he obtained his PhD in Computer Science from Lehigh University. Liangjie has given numerous technical talks at academic conferences as well as industrial meetings. He also co-founded User Engagement Optimization Workshop which has been held in conjunction with CIKM 2013 and KDD 2014. In addition, he was a co-instructor for a tutorial on Online User Engagement: Metrics and Optimization, which has part of WSDM 2018, WWW 2019 and KDD 2020. Liangjie has extensively published papers in recommender systems, search, causal inference and other applied machine learning domains. He has served as senior or program committee members on all major applied machine learning and data mining conferences including KDD, WSDM, WWW, SIGIR, CIKM, EMNLP and ICML.

Mohammed

Mohammed Korayem is a Senior Director of Data Science at CareerBuilder, where he leads the R\&D data science and data engineering teams focused on search, recommendation and AI solutions across the products. His research interests search and recommendation, large-scale visual and textual mining, machine learning, deep learning, computer vision, and soft computing. His research published in WWW, KDD, ACL, ICWSM, NDSS, AAAI, etc. His research covered in media including New Scientist Magazine, MIT Technology Review, Communications of the ACM website, etc. His team received The American Business Awards in Artificial Intelligence/Machine Learning Solutions category for industry-first AI Resume Builder. He obtained his Ph.D. and M.Sc. in Computer Science from Indiana University. He holds multiple patents. He co-organized multiple workshops and conferences including KDD-ORAS: online and adaptative recommender systems (OARS) and Southern Data science conference\footnote{https://www.southerndatascience.com/}.

Haiyan

Haiyan Luo is a VP of Engineering at ZipRecruiter, where he currently leads a world wide group of software engineers, data scientists and engineering managers. Previously, he worked at Indeed, LinkedIn, Yahoo, Cisco and Bell Labs. He received his Ph.D. from the University of Nebraska-Lincoln. He published 40+ academic papers and 10+ patents and several books. He is a IEEE senior member. He also served as the organzier, chair or a TPC member of numerous international magazines, conference and workshhop papers such as KDD, WSDM, INFOCOM, SECON etc. He founded or co-founded several startups. His specialty includes digital advertising, big data systems, recommender systems, financial risk analysis, sponsored content etc.

PROGRAM COMMITTEE MEMBERS

LIST OF TOPICS

We solicit papers describing significant and innovative research and applications to the field of job marketplaces. We invite submissions on a wide range of topics, spanning both theoretical and practical research and applications. Topics include but not limited to:

IMPORTANT DATES

SUBMISSION DETAILS

Following the AAAI 2025 main conference submission & review process, each paper will be reviewed by PC members. The acceptance decisions will take in account novelty, technical depth and quality, insightfulness, depth, elegance, practical or theoretical impact, reproducibility and presentation. We will use double-blind reviewing. For each accepted paper, at least one author must attend the workshop and present the paper.

Submissions are limited to a total of FIVE pages, including all content and references, must be in PDF format, and formatted according to the AAAI standard. Additional information about formatting and style files is available here.

Please submit your paper via Open Review site.

CONTACTS

Please contact organizers with any questions and concerns:

ARCHIVES

Previous editions of the workshop are listed here: