On the predictive analysis of behavioral massive job data using embedded clustering and deep recurrent neural networks

The recent proliferation of social networks as a main source of information and interaction has led to a huge expansion of automatic e-recruitment systems and by consequence the multiplication of web channels (job boards) that are dedicated to job offers disseminating. In a strategic and economic context where cost control is fundamental, it has become necessary to identify the relevant job board for a given new job offer has become necessary. The purpose of this work is to present the recent results that we have obtained on a new job board recommendation system that is a decision-making tool intended to guide recruiters while they are posting a job on the Internet. Firstly, the Doc2Vec embedded representation is used to analyse the textual content of the job offers, then the job applicant clickstreams history on various job boards are stored in a large learning database, and then represented as time series. Secondly, a deep neural network architecture is used to predict future values of the clicks on the job boards. Third, and in parallel, dimensionality reduction techniques are used to transform the clicks numerical time series into temporal symbolic sequences. Forecasting algorithms are then used to predict future symbols for each sequence. Finally, a list of top ranked job boards are kept by maximizing the clickstreams forecasting in both representations. Our experiments are tested on a real dataset, coming from a job-posting database of an industrial partner. The promising results have shown that using deep learning, the recommendation system outperforms standard multivariate models.