  StackOverflow has become an emerging resource for talent recognition in
recent years. While users exploit technical language on StackOverflow,
recruiters try to find the relevant candidates for jobs using their own
terminology. This procedure implies a gap which exists between recruiters and
candidates terms. Due to this gap, the state-of-the-art expert finding models
cannot effectively address the expert finding problem on StackOverflow. We
propose two translation models to bridge this gap. The first approach is a
statistical method and the second is based on word embedding approach.
Utilizing several translations for a given query during the scoring step, the
result of each intermediate query is blended together to obtain the final
ranking. Here, we propose a new approach which takes the quality of documents
into account in scoring step. We have made several observations to visualize
the effectiveness of the translation approaches and also the quality-aware
scoring approach. Our experiments indicate the following: First, while
statistical and word embedding translation approaches provide different
translations for each query, both can considerably improve the recall. Besides,
the quality-aware scoring approach can improve the precision remarkably.
Finally, our best proposed method can improve the MAP measure up to 46% on
average, in comparison with the state-of-the-art expert finding approach.
