Using autoencoders for session-based job recommendations

Emanuel Lacic, Markus Reiter-Haas, Dominik Kowald, Manoj Reddy Dareddy, Junghoo Cho, Elisabeth Lex*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Pages (from-to)617-658
Number of pages42
JournalUser Modeling and User-Adapted Interaction
Volume30
Issue number4
DOIs
Publication statusPublished - 1 Sep 2020

Keywords

  • Accuracy
  • Autoencoders
  • Job recommendations
  • Novelty
  • Session embeddings
  • Session-based recommendation

ASJC Scopus subject areas

  • Education
  • Human-Computer Interaction
  • Computer Science Applications

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