Retrieve and Rank the Experts Using a Cluster-based Translation Model

With respect to the increasing volume and variety of information available on the Web, it is very difficult to find the required knowledge through the massive amount of data. Question-answering systems have been created to make easy knowledge accessing through massive amounts of data. The most important factor in the issue of expert finding is the ability to detect the relationship between query words and documents written by the candidate experts. A challenging issue in this area is the vocabulary gap between query words and the documents of the candidate experts. In this paper, two new translation models are proposed to solve the problem of the vocabulary gap. First model, a cluster-based probabilistic model, and another is based on topic modeling. In these models, the query words are translated into a collection of query-related words, which are written in documents written by more candidate experts. Then, using these words and using a simple composite model, we have retrieved the experts. The proposed models are implemented and evaluated on the Stack overflow test set and finally, we have analyzed the outputs. The results indicate an increase in the Mean Average Precision of the proposed method compared with other methods of expert finding.