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

by   Jun Zhu, et al.

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.


A Knowledge-Enhanced Recommendation Model with Attribute-Level Co-Attention

Deep neural networks (DNNs) have been widely employed in recommender sys...

Predicting Personalized Academic and Career Roads: First Steps Toward a Multi-Uses Recommender System

Nobody knows what one's do in the future and everyone will have had a di...

Tripartite Vector Representations for Better Job Recommendation

Job recommendation is a crucial part of the online job recruitment busin...

Beyond Accuracy Optimization: On the Value of Item Embeddings for Student Job Recommendations

In this work, we address the problem of recommending jobs to university ...

Learning Transferrable Representations of Career Trajectories for Economic Prediction

Understanding career trajectories – the sequences of jobs that individua...

Competence-Level Prediction and Resume Job Description Matching Using Context-Aware Transformer Models

This paper presents a comprehensive study on resume classification to re...

Person-job fit estimation from candidate profile and related recruitment history with co-attention neural networks

Existing online recruitment platforms depend on automatic ways of conduc...

Please sign up or login with your details

Forgot password? Click here to reset