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

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

Abstract

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.

Originalspracheenglisch
TitelRecSys 2019 - 13th ACM Conference on Recommender Systems
Herausgeber (Verlag)Association of Computing Machinery
Seiten496-500
Seitenumfang5
ISBN (elektronisch)9781450362436
DOIs
PublikationsstatusVeröffentlicht - 10 Sep 2019
Veranstaltung13th ACM Conference on Recommender Systems: RecSys 2019 - Copenhagen, Dänemark
Dauer: 16 Sep 201920 Sep 2019

Publikationsreihe

NameRecSys 2019 - 13th ACM Conference on Recommender Systems

Konferenz

Konferenz13th ACM Conference on Recommender Systems
LandDänemark
OrtCopenhagen
Zeitraum16/09/1920/09/19

ASJC Scopus subject areas

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

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