利用深度个性化注意神经网络改进下一步应用预测

近年来，由于电子招聘平台的普及和优势，职位推荐系统得到了广泛的研究。本文讨论了具有许多实际应用价值的下一个作业应用问题。特别是，我们建议利用下一个项目的推荐方法来更好地考虑求职者的职业偏好，以发现他们可能申请的下一个相关的工作职位（简称工作）。我们提出的模型名为个性化注意下一步应用预测(PANAP)，由三个模块组成。第一个模块以无监督的方式从文本内容和元数据属性中学习作业表示。第二个模块学习求职者的表示。它包括一种个性化的注意机制，可以使学习到的职业偏好表现中每个工作的重要性适应特定求职者的形象。注意机制也为学习到的表征带来了一定的可解释性。然后，第三个模块基于表示的相似性将下一个应用预测任务建模为top-k搜索过程。此外，地理位置是影响招聘领域求职者偏好的一个重要因素。因此，我们从负抽样策略的角度，探讨了地理位置对模型性能的影响。在公共职业构建器12数据集上进行的实验表明了人们对我们的方法的兴趣。

原文题目：Improving Next-Application Prediction with Deep Personalized-Attention Neural Network

原文：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.