Talent Recommendation on LinkedIn User Profiles

TL;DR

This paper explores various recommendation techniques to improve matching between LinkedIn user profiles and job opportunities, addressing the challenge of large-scale online recruitment.

Contribution

It evaluates multiple recommendation methods on a large LinkedIn dataset to identify the most effective approach for profile-job matching.

Findings

Certain recommendation algorithms outperform others in accuracy

Large-scale experiments validate the effectiveness of selected methods

Insights into optimal recommendation strategies for online recruitment

Abstract

With the increasing amount of information on the Internet, recommender systems are becoming increasingly crucial in supporting people to find and explore relevant content. This is also true in the online recruitment space, with websites such as LinkedIn, Indeed.com, and Monster.com all using recommender systems. In online recruitment, it can often be challenging for companies to find suitable candidates with appropriate skills because of the huge volume of user profiles available. Identifying users which satisfy a range of different employer needs is also a difficult task. Thus, effective matching of user-profiles and jobs is becoming crucial for companies. This research project applies a wide range of recommendation techniques to the task of user profile recommendation. Extensive experiments are conducted on a large-scale real-world LinkedIn dataset to evaluate their performance, with the aim of identifying the most suitable approach in this particular recommendation scenario.