Mitigating Matching Scarcity in Recruitment Recommendation Domains

  • Alan Cardoso UFSJ
  • Fernando Mourão Seek AIPS
  • Leonardo Rocha UFSJ


A major concern when building Recommender Systems (RSs) for recruitment domains is ensuring actual matching opportunities for all candidates and job vacancies on the system as fast as possible. Indeed, long periods waiting for matchings imply losses of business opportunities, causing financial damages for both candidates and companies. We refer to these scenarios where candidates or job vacancies suffer from the absence of matching opportunities as the Matching Scarcity Problem (MaSP). In this paper, first, we formalize the MaSP itself. Second, we proposed and evaluated strategies to identify automatically candidates and job vacancies suffering from MaSP. Finally, we proposed five heuristic strategies to mitigate MaSP. Our strategies consist of introducing changes in curricula and job descriptions to approximate candidates suffering from MaSP to jobs semantically related to them, and vice versa. The best strategy was able to reduce up to 50% the number of both CVs and jobs suffering from MaSP before applying it in our sample.
Palavras-chave: Job Recommendation, Matching Scarcity, User Modeling
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CARDOSO, Alan; MOURÃO, Fernando ; ROCHA, Leonardo. Mitigating Matching Scarcity in Recruitment Recommendation Domains. In: SIMPÓSIO BRASILEIRO DE SISTEMAS MULTIMÍDIA E WEB (WEBMEDIA), 1. , 2020, Evento Online. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2020 . p. 124-131.

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