Abstract: COVID-19 has a significant impact on all walks of life during the last two years. The traditional recruitment tactics are difficult to put into practice. On the one hand， the recruitment gap is large， on the other hand， job seekers have nowhere to apply for a job. The emergence of online recruitment has brought some convenience to job seekers and recruitment units， but there are still issues such as low efficiency and unbalanced matching betheen person-post. How to execute job matching effectively and swiftly has become an urgent issue that need to be addressed. To solve this problem， a person-posts matching algorithm of Adj-LightGBM based on SMOTE and Bayesian optimization is proposed. Firstly， the post data set is preprocessed. Secondly， SMOTE algorithm is used to over sample the successfully matched samples with a positive-to-negative sample ratio of 1：3. Then， Bayesian optimization is used to find the optimal LightGBM model on the verification set. Finally， the model is tested and evaluated. The optimal Auc and F1-score of the model is 0.974 and 0.970. Compared with support vector machine， random forest and XGBoost algorithm， it is discovered that the proposed algorithm not only has higher accuracy in person-post matching prediction， but also has substantial benefits in model training efficiency.

Key words: person-post matching, unbalanced data, SMOTE, Bayesian optimization, LightGBM