AI and Semantic Modeling for Profile–Opportunity Matching: A Systematic Review with Quantitative Synthesis
Resumo
Digital recruitment platforms and web-based recommender systems have intensified the need for methods that connect academic and professional profiles with job, internship, and career opportunities. However, evidence on profile–opportunity matching remains fragmented across task formulations, datasets, ground-truth sources, validation protocols, and metric families. This paper reports a systematic review with subgroup-based quantitative synthesis of AI-based and semantic modeling approaches for profile–opportunity matching. The review protocol was defined in Parsifal and followed PRISMA reporting recommendations. Searches covered studies published between 2020 and 2026 in ACM Digital Library, IEEE Xplore, ScienceDirect, and Scopus. In total, 425 records were identified; after removing 69 duplicates, 356 records were screened, and 64 studies were selected for full-text assessment. After in-depth reading and methodological quality assessment, 43 studies were retained in the main synthesis. Among these studies, 23 were classified as Strong Include, 18 as Include with limitations, and 2 as Narrative only. Quantitative synthesis eligibility classified 13 studies as Eligible, 28 as Partially eligible, and 2 as Not eligible. The evidence is concentrated in resume–job classification, candidate ranking, job recommendation, fit prediction, and student–job recommendation, using NLP, machine learning, deep learning, BERT/SBERT, Transformers, LLMs, recommender systems, knowledge graphs, ontologies, and graph neural networks. Results also show frequent reliance on private or industrial datasets, which limits reproducibility and direct comparability. Because a single statistical meta-analysis was not methodologically appropriate, the quantitative synthesis separates classification-based fit prediction from Top-K recommendation/ranking, providing a more replicable organization of comparable evidence.
Palavras-chave:
Artificial Intelligence, Semantic Modeling, Profile–Opportunity Matching, Job Recommendation, Internship Recommendation, Resume–Job Matching, Candidate Ranking, Natural Language Processing, Recommender Systems, Knowledge Graphs, Systematic Literature Review, Quantitative Synthesis
Referências
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Publicado
27/10/2026
Como Citar
DUARTE, Jáder Felipe Silva; SOUZA, Adler Diniz de.
AI and Semantic Modeling for Profile–Opportunity Matching: A Systematic Review with Quantitative Synthesis. In: BRAZILIAN SYMPOSIUM ON MULTIMEDIA AND THE WEB (WEBMEDIA), 32. , 2026, Lavras/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 581-594.
DOI: https://doi.org/10.5753/webmedia.2026.30942.
