Should we embed? A study on the online performance of utilizing embeddings for real-time job recommendations

Emanuel Lacic, Markus Reiter-Haas, Tomislav Duricic, Valentin Slawicek, Elisabeth Lex

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review


In this work, we present the fndings of an online study, where we explore the impact of utilizing embeddings to recommend job postings under real-time constraints. On the Austrian job platform Studo Jobs, we evaluate two popular recommendation scenarios: (i) providing similar jobs and, (ii) personalizing the job postings that are shown on the homepage. Our results show that for recommending similar jobs, we achieve the best online performance in terms of Click-Through Rate when we employ embeddings based on the most recent interaction. To personalize the job postings shown on a user's homepage, however, combining embeddings based on the frequency and recency with which a user interacts with job postings results in the best online performance.

Original languageEnglish
Title of host publicationRecSys 2019 - 13th ACM Conference on Recommender Systems
PublisherAssociation of Computing Machinery
Number of pages5
ISBN (Electronic)9781450362436
Publication statusPublished - 10 Sept 2019
Event13th ACM Conference on Recommender Systems: RecSys 2019 - Copenhagen, Denmark
Duration: 16 Sept 201920 Sept 2019

Publication series

NameRecSys 2019 - 13th ACM Conference on Recommender Systems


Conference13th ACM Conference on Recommender Systems


  • BLL Equation
  • Frequency
  • Item Embeddings
  • Job Recommendations
  • Online Evaluation
  • Real-time
  • Recency

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Information Systems
  • Computer Science Applications

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