Leveraging Search History for Improving Person-Job Fit

TL;DR

This paper introduces SHPJF, a model that enhances person-job fit by integrating search history and text content, improving matching accuracy in online recruitment platforms.

Contribution

It presents a novel approach combining BERT-based text encoding and Transformer-based intention modeling, including intention clustering, to leverage search logs for better person-job matching.

Findings

Effective in capturing user job intentions from search logs

Improves person-job fit accuracy on real-world data

Demonstrates significant performance gains over baseline methods

Abstract

As the core technique of online recruitment platforms, person-job fit can improve hiring efficiency by accurately matching job positions with qualified candidates. However, existing studies mainly focus on the recommendation scenario, while neglecting another important channel for linking positions with job seekers, i.e. search. Intuitively, search history contains rich user behavior in job seeking, reflecting important evidence for job intention of users. In this paper, we present a novel Search History enhanced Person-Job Fit model, named as SHPJF. To utilize both text content from jobs/resumes and search histories from users, we propose two components with different purposes. For text matching component, we design a BERT-based text encoder for capturing the semantic interaction between resumes and job descriptions. For intention modeling component, we design two kinds of intention modeling approaches based on the Transformer architecture, either based on the click sequence or query text sequence. To capture underlying job intentions, we further propose an intention clustering technique to identify and summarize the major intentions from search logs. Extensive experiments on a large real-world recruitment dataset have demonstrated the effectiveness of our approach.