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Person-job fit is an essential part of online recruitment platforms inserving various downstream applications like Job Search and CandidateRecommendation. Recently, pretrained large language models have furtherenhanced the effectiveness by leveraging richer textual information in userprofiles and job descriptions apart from user behavior features and jobmetadata. However, the general domain-oriented design struggles to capture theunique structural information within user profiles and job descriptions,leading to a loss of latent semantic correlations. We propose TAROT, ahierarchical multitask co-pretraining framework, to better utilize structuraland semantic information for informative text embeddings. TAROT targetssemi-structured text in profiles and jobs, and it is co-pretained withmulti-grained pretraining tasks to constrain the acquired semantic informationat each level. Experiments on a real-world LinkedIn dataset show significantperformance improvements, proving its effectiveness in person-job fit tasks.

TAROT: A Hierarchical Framework with Multitask Co-Pretraining on Semi-Structured Data towards Effective Person-Job Fit