Abstract
With the popularity of social media, large amounts of data have given us the possibility to learn and build products to optimize certain areas of our existence. In this work, we focus on exploring methods by which we can model the career trajectory of a given candidate, with the help of data mining techniques applied to professional social media data. We first discuss our efforts to normalizing raw data in order to get good enough data for predictive models to be trained. We then report the experiments we conducted. Results show that we can predict job transitions with 67% accuracy when looking at the 10 top predictions.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
The langid [9] toolkit was used: https://github.com/saffsd/langid.py.
References
Al-Otaibi, S.T., Ykhlef, M.: A survey of job recommender systems. Int. J. Phys. Sci. 7(29), 5127–5142 (2012)
Bjelland, M., Fallick, B., Haltiwanger, J., McEntarfer, E.: Employer-to-employer flows in the United States: estimates using linked employer-employee data. J. Bus. Econ. Stat. 29(4), 493–505 (2011)
Boschma, R., Eriksson, R.H., Lindgren, U.: Labour market externalities and regional growth in Sweden: the importance of labour mobility between skill-related industries. Reg. Stud. 48(10), 1669–1690 (2014)
Guerrero, O.A., Axtell, R.L.: Employment growth through labor flow networks. PLOS ONE 8(5), 1–12 (2013). https://doi.org/10.1371/journal.pone.0060808
Heafield, K.: KenLM: faster and smaller language model queries. In: Proceedings of the EMNLP 2011 Sixth Workshop on Statistical Machine Translation, Edinburgh, Scotland, United Kingdom, pp. 187–197, July 2011. https://kheafield.com/papers/avenue/kenlm.pdf
Hu, Y., Koren, Y., Volinsky, C.: Collaborative filtering for implicit feedback datasets. In: Eighth IEEE International Conference on Data Mining, ICDM 2008, pp. 263–272. IEEE (2008)
James, C., Pappalardo, L., Sirbu, A., Simini, F.: Prediction of next career moves from scientific profiles (2018). arXiv preprint: arXiv:1802.04830
Joulin, A., Grave, E., Bojanowski, P., Mikolov, T.: Bag of tricks for efficient text classification (2016). arXiv preprint: arXiv:1607.01759
Lui, M., Baldwin, T.: Langid.py: an off-the-shelf language identification tool. In: Proceedings of the ACL 2012 System Demonstrations, pp. 25–30. Association for Computational Linguistics (2012)
Paparrizos, I., Cambazoglu, B.B., Gionis, A.: Machine learned job recommendation. In: Proceedings of the Fifth ACM Conference on Recommender Systems, pp. 325–328. ACM (2011)
Sundermeyer, M., Schlüter, R., Ney, H.: LSTM neural networks for language modeling. In: Thirteenth Annual Conference of the International Speech Communication Association (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Soliman, Z., Langlais, P., Bourg, L. (2019). Learning Career Progression by Mining Social Media Profiles. In: Meurs, MJ., Rudzicz, F. (eds) Advances in Artificial Intelligence. Canadian AI 2019. Lecture Notes in Computer Science(), vol 11489. Springer, Cham. https://doi.org/10.1007/978-3-030-18305-9_43
Download citation
DOI: https://doi.org/10.1007/978-3-030-18305-9_43
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-18304-2
Online ISBN: 978-3-030-18305-9
eBook Packages: Computer ScienceComputer Science (R0)