Help me find a job: A graph-based approach for job recommendation at scale

Abstract

Online job boards are one of the central components of modern recruitment industry. With millions of candidates browsing through job postings everyday, the need for accurate, effective, meaningful, and transparent job recommendations is apparent more than ever. While recommendation systems are successfully advancing in variety of online domains by creating social and commercial value, the job recommendation domain is less explored. Existing systems are mostly focused on content analysis of resumes and job descriptions, relying heavily on the accuracy and coverage of the semantic analysis and modeling of the content in which case, they end up usually suffering from rigidity and the lack of implicit semantic relations that are uncovered from users' behavior and could be captured by Collaborative Filtering (CF) methods. Few works which utilize CF do not address the scalability challenges of real-world systems and the problem of cold-start. In this paper, we propose a scalable item-based recommendation system for online job recommendations. Our approach overcomes the major challenges of sparsity and scalability by leveraging a directed graph of jobs connected by multi-edges representing various behavioral and contextual similarity signals. The short lived nature of the items (jobs) in the system and the rapid rate in which new users and jobs enter the system make the cold-start a serious problem hindering CF methods. We address this problem by harnessing the power of deep learning in addition to user behavior to serve hybrid recommendations. Our technique has been leveraged by CareerBuilder.com which is one of the largest job boards in the world to generate high-quality recommendations for millions of users.

Learning to Match Jobs with Resumes from Sparse Interaction Data using Multi-View Co-Teaching Network

“…Matching jobs and resumes stands at the core of a recruitment platform. As an important task in recruitment data mining [13,25], person-job fit has been extensively studied in the literature. Early methods cast this problem as a recommendation task [4,17], and the matching capability is obtained based on the collaborative filtering assumption.…”

Job Recommendation System using Content and Collaborative Filtering based Techniques

“…heuristic-based), and ii) model-based methods [4] [5]. They use different similarity measures such as the Jaccard Index [6], Cosine Similarity [7], Euclidean distance [8] and Pearson Correlation Coefficient (PCC) [8] to select applicants or jobs for active applicants. Then, the prediction is done from the ratings of these neighbours or it generates a list of top N jobs as recommendations.…”

A Combined Representation Learning Approach for Better Job and Skill Recommendation

“…In existing works on job recommendation, both collaborative filtering (CF) based methods and content-based filtering methods are used. CF methods use only historical transitions for providing recommendations [1,11,14]. Al-Otaibi et al [1] provided detailed study on job recommendation methods using CF and also discussed challenges and limitations of CF such as sparsity, cold start, etc.…”

“…Large enterprises consider skill re-purposing or skill fungibility for selecting candidates from the existing employees to train them to acquire skills which are short in supply in the market. A number of papers have been published which discuss how the above services are rendered by mining data from resumes and job descriptions [3,4,10,11,14].…”