Abstract
Human resources management is one of the most overriding parts of organizations. They are always willing to hire individuals who meet their requirements while do not impose high costs on the organization. Hence, most organizations, in particular, those which are engaged in Computer Engineering industry are inclined to find and employ individuals who are characterized by their deep disciplinary knowledge in one single area, and their ability to collaborate across different aspects of projects due to their general knowledge in other areas. Nowadays, Community Question Answering i.e. CQA websites are among the best places to find experts. In this study, we propose two models to find and then rank experts with specialty in a specific skill area, as well as general knowledge in the other skill areas i.e. T-shaped users. We estimate the profile diversity of users in our models to detect those who have the aforementioned feature in CQAs, particularly Stackoverflow. Our experiments on three real test collections generated from Stackoverflow’s published data indicate the efficiency of the proposed models in comparison with the state-of-the-art expertise retrieval approach.
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Notes
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In our example, a person who is specialized in front-end development, but also has general knowledge in back-end technologies (or vice versa) is called generalizing specialist.
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Assuming that a full-stack developer is expert in multiple areas.
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For example, recruiters are looking for experts on User Interface rather than jtable or jframe or etc.
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The complete list of skill areas with associated tags is made publicly available at http://bit.ly/tshaped-mining.
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Min-Max Normalization has been applied.
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It has to be noted that other shapes of knowledge (e.g. I-shape, \(\varPi \)-shape) can be defined but for the sake of simplicity, we leave them out in this paper.
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It is worth noting that this probability semantically exposes that skill area which cause candidate e to be T-shaped.
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Logarithm is used to dampen the importance of document count.
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The implementation of our models is available at http://bit.ly/tshaped-mining.
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Gharebagh, S.S., Rostami, P., Neshati, M. (2018). T-Shaped Mining: A Novel Approach to Talent Finding for Agile Software Teams. In: Pasi, G., Piwowarski, B., Azzopardi, L., Hanbury, A. (eds) Advances in Information Retrieval. ECIR 2018. Lecture Notes in Computer Science(), vol 10772. Springer, Cham. https://doi.org/10.1007/978-3-319-76941-7_31
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