Job Recommendation through Progression of Job Selection

TL;DR

This paper presents a machine learning-based job recommendation system that leverages candidate progression, skill embeddings, and multiple sub-recommendations to improve relevance, serendipity, and cold-start issues, achieving high click-through rates.

Contribution

It introduces a novel methodology using progression data, skill embeddings, and blended recommendations, including a Bi-LSTM with attention, for improved job recommendations.

Findings

Achieved the best click-through rate with the proposed model.

Utilized skill embeddings to expand candidate and job coverage.

Demonstrated effectiveness of blended recommendations combining machine learning and sub-recommendations.

Abstract

Job recommendation has traditionally been treated as a filter-based match or as a recommendation based on the features of jobs and candidates as discrete entities. In this paper, we introduce a methodology where we leverage the progression of job selection by candidates using machine learning. Additionally, our recommendation is composed of several other sub-recommendations that contribute to at least one of a) making recommendations serendipitous for the end user b) overcoming cold-start for both candidates and jobs. One of the unique selling propositions of our methodology is the way we have used skills as embedded features and derived latent competencies from them, thereby attempting to expand the skills of candidates and jobs to achieve more coverage in the skill domain. We have deployed our model in a real-world job recommender system and have achieved the best click-through rate through a blended approach of machine-learned recommendations and other sub-recommendations. For recommending jobs through machine learning that forms a significant part of our recommendation, we achieve the best results through Bi-LSTM with attention.