Online Learning For Long-Query Reduction In Interactive Search For Experienced Workers

For domain specific document searches like job matching, long queries are often given as a detailed information of targets. Previous studies found that higher quality results can be obtained by searching with an optimal subset of words excerpted from a long query. To excerpt the optimal subset of words, query reduction using machine learning techniques has been studied. Supervised learning requires training data with annotation, which is especially difficult for in-domain data because of its specific terminology. In this study, we propose a model that integrates machine learning techniques and manual processing for long-query reduction. We integrated our model into a job matching system that collects manual "interactions" and used them as training data to learn query reduction. Furthermore, we evaluated our model with actual job offerings and expert profile data obtained from a recruitment agency. We found that our proposed model outperformed the baseline in precision, recall, and F-measure. The result suggests that our model could be used for query reduction of interactive search systems of specific domain data.