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

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem KonferenzbandBegutachtung


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.

TitelRecSys 2019 - 13th ACM Conference on Recommender Systems
Herausgeber (Verlag)Association of Computing Machinery
ISBN (elektronisch)9781450362436
PublikationsstatusVeröffentlicht - 10 Sep. 2019
Veranstaltung13th ACM Conference on Recommender Systems: RecSys 2019 - Copenhagen, Dänemark
Dauer: 16 Sep. 201920 Sep. 2019


NameRecSys 2019 - 13th ACM Conference on Recommender Systems


Konferenz13th ACM Conference on Recommender Systems

ASJC Scopus subject areas

  • Steuerungs- und Systemtechnik
  • Software
  • Information systems
  • Angewandte Informatik

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