Improving Next-Application Prediction with Deep Personalized-Attention Neural Network - Université Paris-Saclay Access content directly
Conference Papers Year : 2021

Improving Next-Application Prediction with Deep Personalized-Attention Neural Network


Recently, due to the ubiquity and supremacy of E-recruitment platforms, job recommender systems have been largely studied. In this paper, we tackle the next job application problem, which has many practical applications. In particular, we propose to leverage next-item recommendation approaches to consider better the job seeker's career preference to discover the next relevant job postings (referred to jobs for short) they might apply for. Our proposed model, named Personalized-Attention Next-Application Prediction (PANAP), is composed of three modules. The first module learns job representations from textual content and metadata attributes in an unsupervised way. The second module learns job seeker representations. It includes a personalized-attention mechanism that can adapt the importance of each job in the learned career preference representation to the specific job seeker's profile. The attention mechanism also brings some interpretability to learned representations. Then, the third module models the Next-Application Prediction task as a top-K search process based on the similarity of representations. In addition, the geographic location is an essential factor that affects the preferences of job seekers in the recruitment domain. Therefore, we explore the influence of geographic location on the model performance from the perspective of negative sampling strategies. Experiments on the public CareerBuilder12 dataset show the interest in our approach.
Fichier principal
Vignette du fichier
IEEE_ICMLA_camera_ready_arxiv.pdf (6.68 Mo) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-03420596 , version 1 (09-11-2021)



Jun Zhu, Gautier Viaud, Céline Hudelot. Improving Next-Application Prediction with Deep Personalized-Attention Neural Network. IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS, Dec 2021, virtually online, United States. ⟨10.1109/ICMLA52953.2021.00258⟩. ⟨hal-03420596⟩
76 View
85 Download



Gmail Facebook X LinkedIn More