Details
| Original language | English |
|---|---|
| Title of host publication | 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) |
| Editors | Martin Atzmuller, Michele Coscia, Rokia Missaoui |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 266-269 |
| Number of pages | 4 |
| ISBN (electronic) | 978-1-7281-1056-1 |
| ISBN (print) | 978-1-7281-1057-8 |
| Publication status | Published - 2020 |
| Event | 12th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020 - Virtual, Online, Netherlands Duration: 7 Dec 2020 → 10 Dec 2020 |
Abstract
An efficient job recommendation framework needs to recommend an appropriate jobseeker to a recruiter and vice-versa. Prior studies have mostly considered datasets from commercial job portals such as LinkedIn or CareerBuilder. However, these datasets are proprietary and not publicly available. Moreover, these portals charge their clients for offering customized services. Hence, we explore whether publicly available Twitter data can be a viable alternative to commercial job portals. We have extracted 0.76 million job-related tweets. We have manually annotated tweet-pairs from recruiters and jobseekers in the domain of computer science jobs. Next, we have employed Siamese architecture and considered multiple artificial neural network models with different word embeddings. We have achieved around 97% accuracy for some of our models. Our study demonstrates the potential of the Twitter platform for job recommendations.
Keywords
- Job recommendation, Siamese network, Twitter platform, Word embedding
ASJC Scopus subject areas
- Computer Science(all)
- Artificial Intelligence
- Computer Science(all)
- Computer Networks and Communications
- Decision Sciences(all)
- Information Systems and Management
- Psychology(all)
- Social Psychology
- Social Sciences(all)
- Communication
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2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). ed. / Martin Atzmuller; Michele Coscia; Rokia Missaoui. Institute of Electrical and Electronics Engineers Inc., 2020. p. 266-269 9381392.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Matching Recruiters and Jobseekers on Twitter
AU - Khatua, Aparup
AU - Nejdl, Wolfgang
N1 - Funding Information: ACKNOWLEDGMENT Funding for this project was, in part, provided by the European Union’s Horizon 2020 research and innovation program under grant agreement No 832921.
PY - 2020
Y1 - 2020
N2 - An efficient job recommendation framework needs to recommend an appropriate jobseeker to a recruiter and vice-versa. Prior studies have mostly considered datasets from commercial job portals such as LinkedIn or CareerBuilder. However, these datasets are proprietary and not publicly available. Moreover, these portals charge their clients for offering customized services. Hence, we explore whether publicly available Twitter data can be a viable alternative to commercial job portals. We have extracted 0.76 million job-related tweets. We have manually annotated tweet-pairs from recruiters and jobseekers in the domain of computer science jobs. Next, we have employed Siamese architecture and considered multiple artificial neural network models with different word embeddings. We have achieved around 97% accuracy for some of our models. Our study demonstrates the potential of the Twitter platform for job recommendations.
AB - An efficient job recommendation framework needs to recommend an appropriate jobseeker to a recruiter and vice-versa. Prior studies have mostly considered datasets from commercial job portals such as LinkedIn or CareerBuilder. However, these datasets are proprietary and not publicly available. Moreover, these portals charge their clients for offering customized services. Hence, we explore whether publicly available Twitter data can be a viable alternative to commercial job portals. We have extracted 0.76 million job-related tweets. We have manually annotated tweet-pairs from recruiters and jobseekers in the domain of computer science jobs. Next, we have employed Siamese architecture and considered multiple artificial neural network models with different word embeddings. We have achieved around 97% accuracy for some of our models. Our study demonstrates the potential of the Twitter platform for job recommendations.
KW - Job recommendation
KW - Siamese network
KW - Twitter platform
KW - Word embedding
UR - http://www.scopus.com/inward/record.url?scp=85103685161&partnerID=8YFLogxK
U2 - 10.1109/ASONAM49781.2020.9381392
DO - 10.1109/ASONAM49781.2020.9381392
M3 - Conference contribution
AN - SCOPUS:85103685161
SN - 978-1-7281-1057-8
SP - 266
EP - 269
BT - 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)
A2 - Atzmuller, Martin
A2 - Coscia, Michele
A2 - Missaoui, Rokia
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020
Y2 - 7 December 2020 through 10 December 2020
ER -