Deploying CNN-Bi LSTM Model for Talent Acquisition Analytics in HR Management

Talent Acquisition Analytics is a special area within HR Management to analyse and put into practice. With these techniques, analysts are able to make accurate decisions in recruitment and hiring of employees. With the advance algorithms and machine learning models, about valuable insights can be generated from resumes and lead to more objective and informed evaluation of candidates. However, these methods are found to have certain disadvantages like ignoring complex candidate variables, displaying biases, and a lack of adaptability to anyhow changing circumstances of the labour market. The present study proposes a different approach by putting forward a CNN-BiLSTM Model for Talent Acquisition Analytics, hereby focusing on resume analysis. This model incorporates CNN for spatial feature extraction and Bi-LSTM for sequential comprehension to enhance pattern recognition and prediction accuracy in the hiring process. The result of the study is implemented in a tool using Python. On average, it shows 13.7% higher accuracy with the CNN-BiLSTM model, as compared to the SVM and KNN methods, indicating its much higher credential than any other method in Talent Acquisition Analytics. The implementation also provides a friendly interface warranting its easy integration, thereby showcasing the applicability and real-world relevance of the proposed approach in HR Management.