DeepMatch: A BERT-powered talent matchmaking approach

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dc.authorid0000-0003-0261-4404
dc.authorid0009-0008-7607-8193
dc.authorid0009-0000-4539-3029
dc.authorid0000-0001-5639-1408
dc.contributor.authorGözüaçık, Necip
dc.contributor.authorTopaloğlu, Atakan
dc.contributor.authorEvren, Ayse Mine
dc.contributor.authorKarakuş, Serkan
dc.contributor.editorAkram Bennour
dc.contributor.editorAhmed Bouridane
dc.contributor.editorSomaya Almaadeed
dc.contributor.editorBassem Bouaziz
dc.contributor.editorEran Edirisinghe
dc.date.accessioned2025-04-22T13:22:13Z
dc.date.available2025-04-22T13:22:13Z
dc.date.issued2025
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Yazılım Mühendisliği Bölümü
dc.description.abstractConsultancy 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.
dc.identifier.citationGözüaçık, N., Topaloğlu, A., Evren, A. M., Karakuş, S. (2025). DeepMatch: A BERT-powered talent matchmaking approach. Bennour, A., Bouridane, A., Almaadeed, S., Bouaziz, B., Edirisinghe, E. (eds) Intelligent Systems and Pattern Recognition. ISPR 2024. Communications in Computer and Information Science, 2303, pp.190-199. https://doi.org/10.1007/978-3-031-82150-9_15
dc.identifier.doi10.1007/978-3-031-82150-9_15
dc.identifier.endpage199
dc.identifier.isbn9783031821493
dc.identifier.isbn9783031821509
dc.identifier.issn1865-0929
dc.identifier.issn1865-0937
dc.identifier.scopus2-s2.0-105000630799
dc.identifier.scopusqualityQ3
dc.identifier.startpage190
dc.identifier.urihttps://hdl.handle.net/20.500.13055/958
dc.identifier.urihttps://doi.org/10.1007/978-3-031-82150-9_15
dc.identifier.volume2303
dc.indekslendigikaynakScopus
dc.institutionauthorGözüaçık, Necip
dc.institutionauthorid0000-0003-0261-4404
dc.language.isoen
dc.publisherSpringer Nature Link
dc.relation.ispartofIntelligent Systems and Pattern Recognition
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectDeep Learning
dc.subjectJonker–Volgenant Algorithm
dc.subjectNatural Language Processing
dc.subjectTalent Management
dc.subjectText Similarity
dc.titleDeepMatch: A BERT-powered talent matchmaking approach
dc.typeConference Object
dspace.entity.typePublication

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