Intelligent Recruitment: Deep Semantic Matching, Interview Assistance, and Text Representation

Talent is a core competitive advantage for enterprises, and traditional recruitment suffers from high cost and low efficiency. Applying natural language processing (NLP) to intelligent recruitment can significantly enhance talent management.

The work is divided into four parts: background of intelligent recruitment, person‑job matching, interview assistance, and recruitment text representation.

For person‑job matching, the problem is formalized as a text matching task between job descriptions and resumes. A deep semantic matching model is proposed, consisting of word‑level representations via bidirectional LSTM, four attention mechanisms (single/multi‑ability for job and candidate), and a final matching prediction module with a re‑training pool layer.

Extensive experiments on real hiring data show the model achieves the best performance on both matching and recommendation tasks, and also highlights the impact of sensitive features such as gender, recommending their exclusion to avoid bias.

In interview assistance, a topic‑based approach (JLMIA) is introduced to model the semantic spaces of job descriptions, resumes, and interview evaluations. Neural‑JLMIA and R‑JLMIA extend this with neural variational inference, enabling dynamic modeling of topic evolution across interview rounds.

For recruitment text representation, variational autoencoders (VAE) are examined, and a novel DU‑VAE architecture is designed to mitigate posterior collapse by encouraging diversity (MPD) and reducing uncertainty (conditional entropy) through dropout and batch normalization.

Experimental results demonstrate that DU‑VAE preserves the geometry of the latent space better than standard VAE, achieving superior generation quality and downstream classification performance.

The Q&A section discusses practical issues such as ensuring clear job descriptions, detecting fabricated resumes, and automatically generating interview questions based on extracted skill topics.