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

TL;DR

This paper introduces PANAP, a deep neural network model that predicts the next job application by learning personalized representations of job seekers and jobs, incorporating geographic location and interpretability.

Contribution

The paper presents a novel personalized-attention neural network for next-application prediction that integrates textual, metadata, and location data with interpretability features.

Findings

Outperforms existing methods on CareerBuilder12 dataset

Effectively incorporates geographic location into predictions

Provides interpretable representations of job seeker preferences

Abstract

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.