Combining Career Progression and Profile Matching in a Job Recommender System

Abstract

In this paper we consider the problem of job recommendation, suggesting suitable jobs to users based on their profiles. We compare a baseline method treating users and jobs as documents, where suitability is measured using cosine similarity, with a model that incorporates job transitions trained on the career progressions of a set of users. We show that the job transition model outperforms cosine similarity. Furthermore, a cascaded system combining career transitions with cosine similarity generates more recommendations of a similar quality. The analysis is conducted by examining data from 2,400 LinkedIn users, and evaluated by determining how well the methods predict users’ current positions from their profiles and previous position history.