Language Modelling for Collaborative Filtering: Application to Job Applicant Matching

This paper is concerned with collaborative retrieval. Specifically, it aims to address the so-called frictional unemployment phenomenon, and recommend job ads to applicants. Two proprietary databases are considered; they reflect the context of unskilled low-paid jobs/applicants on the one hand, and highly qualified jobs/applicants on the other hand, including the job ads and applicant resumes together with the collaborative filtering data recording the applicant clicks on job ads. The proposed approach, called LaJam, focuses on the semi-cold start recommendation problem of recommending new job ads to known applicants. This setting is relevant to the temporary job sector, of increasing importance for current job markets. LaJam learns a continuous language model on the job ad space, trained to comply with the collaborative filtering metrics. This language model, implemented as a neural net, can flexibly take into account heterogeneous additional information, e.g. related to the posting time and geolocation of the job adsThe merits of the LaJam approach are demonstrated comparatively to the state of the art on the public CiteULike dataset. The comparison of the CiteULike dataset with the proprietary datasets sheds some light on the specific difficulties of the job applicant matching problem.