Renold Raj Devaraj
ABSTRACT
Performance Management1 is of paramount importance in Human Resources Planning. To address today's growing skill challenge, employers need to determine existing employee expertise deficit to achieve much needed business outcomes. Determining skill gap amongst employees helps the resource planners to decide between revising their hiring procedures to not overlook expert talent and expansion of their training programs for existing employees with the required skills to enhance their capacity to deliver future projects. This paper explores a well-known unsupervised learning algorithm: Collaborative filtering to predict and prioritise the employees across their domain expertise based on multiple factors such as existing domain expertise, location of the employees, availability of the employees, relevant learnings, prior experience and work performance to handle the skill specific projects in the future. Employee prioritisation for the future across domain specific expertise using this unsupervised machine learning algorithm will potentially help in optimizing the skilled resource pool and make the resource planning a bit seamless for operational resource managers in any industry vertical.
- Chen W-H, Hsu C-C, Lai Y-A, Liu V, Yeh M-Y and Lin S-D (2020) Attribute-Aware Recommender System Based on Collaborative Filtering: Survey and Classification. Front. Big Data 2:49. doi: 10.3389/fdata.2019.00049Google Scholar
Cross Ref
- Margherita, A. 2022. Human resources analytics: A systematization of research topics and directions for future research. Human resource management review. 32, 2 (2022), 100795. DOI:https://doi.org/10.1016/j.hrmr.2020.100795.Google Scholar
- Padilla-Vega, R.E. et al. 2020. Workforce planning and management FIT in call centers. Strategic HR review. 19, 1 (2020), 37--40. DOI:https://doi.org/10.1108/shr-02-2020-176.Google Scholar
- Parhi, P. et al. 2017. A survey of methods of collaborative filtering techniques. 2017 International Conference on Inventive Systems and Control (ICISC) (2017).Google ScholarCross Ref
- Sarwar, B. et al. 2001. Item-based collaborative filtering recommendation algorithms. Proceedings of the 10th international conference on World Wide Web (New York, NY, USA, 2001).Google ScholarDigital Library
- Zhe Cao, Tao Qin, Tie-Yan Liu, Ming-Feng Tsai, and Hang Li. 2007. Learning to rank: From pairwise approach to listwise approach. In Proceedings of the 24th International Conference on Machine Learning (ICML). 129--136.Google Scholar
Index Terms
- Machine Learning driven Skill Prioritisation for Human Resource Planning
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