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DeepMatch: A BERT-powered talent matchmaking approach
(Springer Nature Link, 2025) Gözüaçık, Necip; Topaloğlu, Atakan; Evren, Ayse Mine; Karakuş, Serkan; Akram Bennour; Ahmed Bouridane; Somaya Almaadeed; Bassem Bouaziz; Eran Edirisinghe
Consultancy companies aim to match their employees to customer projects based on their employee’s talents. Traditional matchmaking methodologies are founded on manual processes that rely on rules of thumb or algorithms that are based on handcrafted heuristics, which cause the matchings to be not only sub-optimal, but also time-consuming, subjective, and prone to human errors. In this paper, we propose a novel consultancy matching algorithm that utilizes BERT to semantically find the most optimal consultant-project matchings for a given set of consultants and projects, pairing relevant project specifications with consultant specifications using the JVSAP algorithm. In doing so, our proposed talent matchmaking system may be utilized to improve the accuracy and efficiency of consultancy matching, thereby facilitating more effective consultancy engagements. Our findings suggest that the pairings demonstrate a discernible alignment with human intuition, as evidenced by the consistent correlation between consultants possessing domain-specific expertise and projects characterized by corresponding thematic descriptions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.