A Language Model based Job Recommender

Abstract

Matching candidates to job openings is a hard real world problem of economic interest that thus far de es researchers' attempts to tackle it. Collaborative ltering methods, which have proven to be highly e ective in other domains, have a di cult time nding success when applied to Human Resources. Aside from the well known cold-start issue there are other problems speci c to the recruitment world that explain the poor results attained. In particular, fresh job openings arrive all the time and they have relatively short expiration periods. In addition, there is a large volume of passive users who are not actively looking for a job, but that would consider a change if a suitable o er came their way. The two constraints combined suggest that content based models may be advantageous. Previous attempts to attack the problem have tried to infer relevance from a variety of sources. Indirect information captured from web server and search engine logs, as well as eliciting direct feedback from users or recruiters have all been polled and used to construct models. In contrast, this thesis departs from previous methods and tries to exploit resume databases as a primary source for relevance information, a rich resource that in my view remains greatly underutilized. Relevance models are adapted for the task at hand and a formulation is derived to model job transitions as a Markov process, with the justi cation being based on David Ricardo's principle of comparative advantage. Empirical results are compiled following the Cran eld benchmarking methodology and compared against several standard competing algorithms