  Job search through online matching engines nowadays are very prominent and
beneficial to both job seekers and employers. But the solutions of traditional
engines without understanding the semantic meanings of different resumes have
not kept pace with the incredible changes in machine learning techniques and
computing capability. These solutions are usually driven by manual rules and
predefined weights of keywords which lead to an inefficient and frustrating
search experience. To this end, we present a machine learned solution with rich
features and deep learning methods. Our solution includes three configurable
modules that can be plugged with little restrictions. Namely, unsupervised
feature extraction, base classifiers training and ensemble method learning. In
our solution, rather than using manual rules, machine learned methods to
automatically detect the semantic similarity of positions are proposed. Then
four competitive "shallow" estimators and "deep" estimators are selected.
Finally, ensemble methods to bag these estimators and aggregate their
individual predictions to form a final prediction are verified. Experimental
results of over 47 thousand resumes show that our solution can significantly
improve the predication precision current position, salary, educational
background and company scale.
