@inproceedings{ca0fa16c7fd04d1eb539d2771d1e9b10,
title = "Should we embed? A study on the online performance of utilizing embeddings for real-time job recommendations",
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.",
keywords = "BLL Equation, Frequency, Item Embeddings, Job Recommendations, Online Evaluation, Real-time, Recency",
author = "Emanuel Lacic and Markus Reiter-Haas and Tomislav Duricic and Valentin Slawicek and Elisabeth Lex",
year = "2019",
month = sep,
day = "10",
doi = "10.1145/3298689.3346989",
language = "English",
series = "RecSys 2019 - 13th ACM Conference on Recommender Systems",
publisher = "Association of Computing Machinery",
pages = "496--500",
booktitle = "RecSys 2019 - 13th ACM Conference on Recommender Systems",
address = "United States",
note = "13th ACM Conference on Recommender Systems : RecSys 2019 ; Conference date: 16-09-2019 Through 20-09-2019",
}