Human Resource Recommendation Based on CBCF-BAC and Short Text Similarity Algorithm

Nowadays, many job seekers often cannot clearly analyze their job advantages and the real needs of the position, which can easily lead to large job seeking deviations, low admission rates, and waste of talent in the human resources market. Therefore, starting from the perspective of textual information, this study introduced a bidirectional long short-term memory network and attention mechanism based on the ChineseBERT module to optimize data feature extraction. At the same time, based on the short text similarity algorithm, the similarity calculation was improved, and a new human resource recommendation model was finally proposed. The experimental outcomes denoted that the data classification accuracy of the new model was as high as 97.3%, which was about 5% higher than that of the ChineseBERT module alone. The highest recommendation success rate was 97.6%, the highest job recommendation acceptance rate was 97%, the highest job matching degree was 97.83%, the lowest average processing time was 3.28 seconds, and the highest user satisfaction was 98.88%. From this, the model proposed by the research has excellent performance in occupational data classification and human resource recommendation among many existing models, and can provide an effective technical support for subsequent human resource recommendations and market operations.