  In this work, we present the findings 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.
