A Combined Representation Learning Approach for Better Job and Skill Recommendation

Abstract

Job recommendation is an important task for the modern recruitment industry. An excellent job recommender system not only enables to recommend a higher paying job which is maximally aligned with the skill-set of the current job, but also suggests to acquire few additional skills which are required to assume the new position. In this work, we created three types of information networks from the historical job data: (i) job transition network, (ii) job-skill network, and (iii) skill co-occurrence network. We provide a representation learning model which can utilize the information from all three networks to jointly learn the representation of the jobs and skills in the shared k-dimensional latent space. In our experiments, we show that by jointly learning the representation for the jobs and skills, our model provides better recommendation for both jobs and skills. Additionally, we also show some case studies which validate our claims.

A Survey on Skill Identification From Online Job Ads

“…For the same purpose, in [42], the authors built a job understanding model to improve the representation of jobs and proposed an improvement for the job posting flow in LinkedIn. In [132], the authors built a job recommender that jointly learns the representation of the jobs and skills in the shared k-dimensional latent space of job transition network, job-skill network, and skill co-occurrence network. In a similar way, the authors of [133] proposed a recommender system that, starting from a set of users' skills, identifies the most suitable jobs as they emerge from a large dataset of online IT job ads, which were processed and represented as a graph of occupations and skills.…”

Embedding-based Recommender System for Job to Candidate Matching on Scale

“…Traditionally, semantic matching using job title and skill entities has been the focuses for job classification and job recommendation tasks. Herein, we have taken advantage of a representation learning model that utilizes the information graph from job transition network, job-skill network and skill co-occurrence network [6]. The model used both Bayesian personalized ranking and margin-based loss functions to learn the vector representation for the semantic entities and allow us to encode the local neighborhood structures captured by the information graphs.…”

“…To address this challenge, we have proposed a two-staged recommendation system using an embedding-based approach (Figure 3). A fused embedding strategy that applies deep learning [4,5], representation learning with job-skill information graph [6] and geolocation calculator [7] techniques are used for both job and candidate. We have also implemented Faiss index for clustering and compressing the embeddings, which also allows us to conduct the approximate nearest neighbor search for candidate retrieval on runtime [8,9].…”

Improving Next-Application Prediction with Deep Personalized-Attention Neural Network

“…The recommendation is then modeled as a top-K search based on these learned embeddings using similarity-based algorithms. For example, [7] uses graphs to learn job title and skills representations. [8] proposes a collective multi-view method to learn job title representations.…”