In this work, we address the problem of providing job recommendations in an online session setting, in which we do not have full user histories. We propose a recommendation approach, which uses different autoencoder architectures to encode sessions from the job domain. The inferred latent session representations are then used in a k-nearest neighbor manner to recommend jobs within a session. We evaluate our approach on three datasets, (1) a proprietary dataset we gathered from the Austrian student job portal Studo Jobs, (2) a dataset released by XING after the RecSys 2017 Challenge and (3) anonymized job applications released by CareerBuilder in 2012. Our results show that autoencoders provide relevant job recommendations as well as maintain a high coverage and, at the same time, can outperform state-of-the-art session-based recommendation techniques in terms of system-based and session-based novelty.

中文翻译：

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使用自动编码器进行基于会话的工作推荐

在这项工作中，我们解决了在没有完整用户历史记录的在线会话设置中提供工作推荐的问题。我们提出了一种推荐方法，它使用不同的自动编码器架构来编码来自作业域的会话。然后以 k 最近邻方式使用推断的潜在会话表示来推荐会话中的工作。我们在三个数据集上评估我们的方法，（1）我们从奥地利学生工作门户 Studo Jobs 收集的专有数据集，（2）XING 在 RecSys 2017 挑战赛后发布的数据集和（3）CareerBuilder 在 2012 年发布的匿名工作申请. 我们的结果表明，自动编码器提供了相关的工作推荐并保持了高覆盖率，同时，