  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.
