Modeling Professional Influence and Hiring Outcomes Using Multimodal Generative AI and Knowledge-Graph–Augmented Reasoning
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V4I3P117Keywords:
Multimodal Generative AI, Knowledge Graphs, Hiring Prediction, Professional Influence Modeling, Graph Neural Networks, Recruitment Analytics, Retrieval-Augmented Generation, Explainable AI, Workforce Intelligence, AI EthicsAbstract
The accelerating process of professional ecosystem digitization has altered the way organizations assess talent and the way persons build professional leverage. The contemporary recruitment instruments are dependent more and more on digital footprint including resumes, portfolios, online actions, employment networking activity, recommendations, multimedia presentations, and recording of interviews. Conventional hiring analytics systems, though, are still cheated in the synthesis of heterogeneous data modalities, or in logic among relational professional designs. The current paper suggests a common computational architecture that presents professional influence and recruitment performance through Multimodal Generative Artificial Intelligence (AI) combined with Knowledge-Graph -based Reasoning (KGAR). The suggested architecture uses big multimodal foundation models to encode structural and non-structural candidate data such as text, video, speech, behavioral metadata and network graphs. The knowledge graph instance of professional connections, organizational schemas, abilities taxonomies, professional associations, and domain networks facilitates reasoning in contexts and prevents hallucination as a result of structured constraint propagation. The study proposes a probabilistic influence modeling tool that measures professional credibility and propensity to get hired based upon graph centrality scores, multimodal embedding similarity scores, and generative confidence scores. It combines Relational inference and Retrieval-augmented generation (RAG) based on Graph Neural Networks (GNNs) to base hiring recommendations on validated knowledge entities. Simulated enterprise-scale recruitment datasets of textual corpora on the CVs, video interview transcripts, professional network graphs and skill ontologies are experimentally validated. The findings indicate that, relative to a transformer-only baseline model, knowledge-graph-enabled multimodality generative reasoning enhances hiring prediction accuracy, decreases variance in bias, and increases explainability. The suggested framework is relevant to three areas, including (1) AI-based recruitment analytics, (2) influence modeling in professional networks, and (3) interpretable multimodal generative systems. Ethical issues, fairness limitations and regulatory compliance requirements are also taken care of in the study. Combining an orderly reasoning with generative intelligence, the system can produce strong evaluations of candidates when things are not certain and retain transparency. This indicates that multimodal generative AI computer systems along with the knowledge graphs can reinvent the hiring intelligence systems so as to succeed in generating postulates-based decision support and equitable workforce analytics. The study has given methodology underpinnings, modeling equation, evaluation metrics used, and implementation principles of scalable enterprise implementation.
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