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Automatic Curriculum Recommendation for Employees

  • Conference paper
  • First Online:
Machine Learning, Optimization, and Data Science (LOD 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12566))

  • 1321 Accesses

Abstract

We describe a platform for recommending training assets to employees based on interests, career objectives, organizational hierarchy, and stakeholder or peer feedback. The system integrates content-based and interested-based recommendations across multiple data-streams and interaction modalities to arrive at superior recommendations to those based on just content or interests. The training assets span a wide variety of content formats such as blog articles, podcasts, videos, books, and summaries with the added complexity of multiple content providers. The system incorporates a gamut of interactive information such as likes, ratings, comments, and social activity such as sharing to further personalize recommendations to the user. The system has been deployed in a large organization and is continuously improving from user feedback.

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Notes

  1. 1.

    https://lucene.apache.org/.

  2. 2.

    The system titled “Enhancing Employee Development Through Personalized Learning Recommendations” was awarded the highest honors, the Gold awards, at the Brandon Hall Excellence in Technology Program in the categories “Best Advance in Performance Support Technology” and “Best Advance in Learning Management Technology” https://www.brandonhall.com/excellenceawards/excellence-technology.php?year=2018.

  3. 3.

    https://nlp.stanford.edu/IR-book/html/htmledition/document-and-query-weighting-schemes-1.html.

  4. 4.

    https://www.drupal.org/.

  5. 5.

    https://www.mysql.com/.

  6. 6.

    https://www.python.org/.

  7. 7.

    https://numpy.org/.

  8. 8.

    https://www.scipy.org/.

  9. 9.

    https://textblob.readthedocs.io/en/dev/quickstart.html#sentiment-analysis.

References

  1. Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20, 273–297 (1995). https://doi.org/10.1007/BF00994018

    Article  MATH  Google Scholar 

  2. Fellbaum, C.: WordNet: An Electronic Lexical Database. Bradford Books, London (1998)

    Book  MATH  Google Scholar 

  3. Harpale, A.S., Yang, Y.: Personalized active learning for collaborative filtering. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2008, pp. 91–98. Association for Computing Machinery, New York (2008). https://doi.org/10.1145/1390334.1390352

  4. Hastie, T., Mazumder, R., Lee, J.D., Zadeh, R.: Matrix completion and low-rank SVD via fast alternating least squares. J. Mach. Learn. Res. 16(1), 3367–3402 (2015)

    MathSciNet  MATH  Google Scholar 

  5. Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems (2009)

    Google Scholar 

  6. Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval. Cambridge University Press, Cambridge (2008)

    Book  MATH  Google Scholar 

  7. Micarelli, A., Gasparetti, F., Sciarrone, F., Gauch, S.: Personalized search on the world wide web. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web. LNCS, vol. 4321, pp. 195–230. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-72079-9_6

    Chapter  Google Scholar 

  8. Nocedal, J., Wright, S.J.: Numerical Optimization, 2nd edn. Springer, New York (2006). https://doi.org/10.1007/978-0-387-40065-5

    Book  MATH  Google Scholar 

  9. Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014). http://www.aclweb.org/anthology/D14-1162

  10. Řehůřek, R., Sojka, P.: Software framework for topic modelling with large corpora. In: Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks, pp. 45–50. ELRA, Valletta, Malta, May 2010. http://is.muni.cz/publication/884893/en

  11. Schafer, J.B., Frankowski, D., Herlocker, J., Sen, S.: Collaborative filtering recommender systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web. LNCS, vol. 4321, pp. 291–324. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-72079-9_9

    Chapter  Google Scholar 

  12. Shalev-shwartz, S., Tewari, A.: Stochastic methods for 1-regularized loss minimization. J. Mach. Learn. Res. 12, 1865–1892 (2011)

    MathSciNet  MATH  Google Scholar 

  13. Virtanen, P., et al.: SciPy 1.0-Fundamental Algorithms for Scientific Computing in Python. arXiv e-prints arXiv:1907.10121, July 2019

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Author information

Authors and Affiliations

  1. GE Global Research, Niskayuna, USA

    Abhay Harpale

Authors
  1. Abhay Harpale
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Corresponding author

Correspondence to Abhay Harpale .

Editor information

Editors and Affiliations

  1. University of Catania, Catania, Italy

    Giuseppe Nicosia

  2. University of Reading, Reading, UK

    Varun Ojha

  3. University of Oxford, Oxford, UK

    Emanuele La Malfa

  4. University of Cambridge, Cambridge, UK

    Giorgio Jansen

  5. Almawave, Rome, Italy

    Vincenzo Sciacca

  6. University of Florida, Gainesville, FL, USA

    Panos Pardalos

  7. University of Catania, Catania, Italy

    Giovanni Giuffrida

  8. Harvard University, Cambridge, MA, USA

    Renato Umeton

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Harpale, A. (2020). Automatic Curriculum Recommendation for Employees. In: Nicosia, G., et al. Machine Learning, Optimization, and Data Science. LOD 2020. Lecture Notes in Computer Science(), vol 12566. Springer, Cham. https://doi.org/10.1007/978-3-030-64580-9_12

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  • DOI: https://doi.org/10.1007/978-3-030-64580-9_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-64579-3

  • Online ISBN: 978-3-030-64580-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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