# Should we Embed? A Study on the Online Performance of Utilizing Embeddings for Real-Time Job Recommendations

arXiv: https://arxiv.org/abs/1907.06556 · HTML: https://mlsanity.com/paper/1907.06556/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jul 2019.

## Built on by (most cited first, 2 of 2)

- [Transformers Meet ACT-R: Repeat-Aware and Sequential Listening Session Recommendation](https://mlsanity.com/paper/2408.16578/) (2024)
- [Skill matching at scale: freelancer-project alignment for efficient multilingual candidate retrieval](https://mlsanity.com/paper/2409.12097/) (2024)

## Abstract (arXiv)

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.