Intelligent candidate recommendation system based on experimental calculation of the similarity model

This article describes a system that can significantly improve the candidate selection process for media casting. It allows the impresario company to recommend an optimal set of performers by selecting people who are most likely to fit specific role demands. Several mechanisms interoperate to provide an innovative recommendation system. These include Osgood semantic scale, various NLP techniques and searching in an embedding space. The Polish language corpus for NLP was created using PolishSubtitles, Dictionary of Synonyms, and Dictionary created from Personnel Consulting Agency Database, provided for the authors by cast recruitment company. Before artificial intelligence training, data had to be segmented, lemmatized, and disambiguated in the morph syntactic domain using the Morpheus 2.0 analyzer. The recommendation system described in the paper is very promising due to the applicability of machine learning algorithms and the intelligent user interface. A test, performed by a cast recruitment company, was performed to confirm proposed model viability. Test was concluded in comparision to manual selection process and provided more adequate set of candidates in shorter timeframe.