Written by Antonio Andrea Gentile
The task of finding the right employee among active personnel for a vacant position is difficult for organizations of all sizes. In large businesses, the Human Resources department does not know everyone in the company and their skills making the decision process painfully slow and cumbersome. Recommending someone for a job requires profiling the candidates according to their skills, past activities and competency areas and it requires an understanding of how well they integrate in existing teams.
provides a graph data model that simulates this problem, and outlines possible semi-automated solutions which can be implemented with the Cypher query language. These solutions draw on both collaborative and content-based filtering, and demonstrate how both can be used to handle different situations. A content-based approach performs well in a cold-start situations, while a recommendation system based on collaborative filtering provides better ranking when information is collected about employees’ skills and competencies from colleagues. A simple internal competence management tool based in Neo4j is provided that may be used for everyday tasks such as the organization of training, or encouraging cross-evaluation among the personnel.
Leveraging both, and the advantages of a graph database, the hiring committee can quickly and efficiently explore the recommendations for candidates satisfying one or more requirements.
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