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Artikkeleita Työpoliittinen aikakauskirja 1/2018
Abel F., Benczúr A., Kohlsdorf D., Larson M., ja Pálovics R. (2016). RecSys Challenge 2016: Job Recommendations. In Proceedings of the 10th ACM Conference on Recommender Systems (RecSys ’16). ACM, New York, NY, USA, 425-426.
Al-Otaibi S. ja Ykhlef M. (2012). A
survey of job recommender systems. International Journal of Physical Sciences 7.29: 5127-5142.
Deloitte (2017). Big data kohtaannon edistäjänä. Julkaisematon raportti.
Gasser A. (2017). A Dive into Stack Over ow Jobs Search, https://medium. com/@aurelien.gasser/a-dive-into-stack- over ow-jobs-search-62bc6e628f83
Gupta A. (2012). Jobscan Automated CV- Vacancy Matching and Improved Search in a Vacancy Database.
KEHA-keskus, Toiminnan kehittämisyksikkö. (2016). TE-palvelujen ammattinimikkeet ja –kuvaukset. http:// www.te-palvelut. /te/ /pdf/ISCO.pdf
Loh W. Y. (2011). Classi cation and regression trees. Wiley Interdisciplinary Reviews: Data Mining and Knowledge DISCOvery, 1(1), 14-23.
Lu Y., El Helou S. ja Gillet D. A recommender system for job seeking and recruiting website. Proceedings of the 22nd International Conference on World Wide Web. ACM, 2013.
Menze B., Kelm B., Masuch R., Himmelreich U., Bachert P., Petrich W. ja Hamprecht F. ”A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classi cation of spectral data.” BMC bioinformatics 10.1 (2009): 213.
      Opetushallituksen tilastopalvelu. (2017). Työllisten ammatit ja koulutus. Haettu toukokuun 18., 2017. https://vipunen. /  - /rakenne/Sivut/Ty%C3%B6llisten- ammatit-ja-koulutus.aspx
Polamuri, S. (2017). How the random forest algorithm works in machine learning. http://dataaspirant. com/2017/05/22/random-forest- algorithm-machine-learing/
Posse, C. (2016). Cloud Jobs API: machine learning goes to work on job search and dISCOvery. https://cloud. cloud-jobs-api-machine-learning-goes- to-work-on-job-search-and-dISCOvery
Preetha, A. (2016). Algorithms and architecture for job recommendations.
Wright M. and Ziegler A. (2017). ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R. Journal of Statistical Software, 77(1), 1-17
Wu L., Shah S., Choi S., Tiwari M. ja Posse C. (2014). The browsemaps: Collaborative  ltering at LinkedIn. CEUR Workshop Proceedings. 1271.

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