Lacic Emanuel, Reiter-Haas Markus, Duricic Tomislav, Slawicek Valentin, Lex Elisabeth
2019
Should we Embed? A Study on the Online Performance of Utilizing Embeddings for Real-Time Job Recommendations
ACM Conference on Recommender Systems (RecSys2019) ACM Copenhagen, Denmar
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.
Lacic Emanuel, Kowald Dominik, Reiter-Haas Markus, Slawicek Valentin, Lex Elisabeth
2018
Beyond Accuracy Optimization: On the Value of Item Embeddings for Student Job Recommendation
In Proceedings of the International Workshop on Multi-dimensional Information Fusion for User Modeling and Personalization (IFUP'2018) co-located with the 11th ACM International Conference on Web Search and Data Mining WSDM'2018 ACM Los Angeles, USA
In this work, we address the problem of recommending jobs touniversity students. For this, we explore the impact of using itemembeddings for a content-based job recommendation system. Fur-thermore, we utilize a model from human memory theory to integratethe factors of frequency and recency of job posting interactions forcombining item embeddings. We evaluate our job recommendationsystem on a dataset of the Austrian student job portal Studo usingprediction accuracy, diversity as well as adapted novelty, which isintroduced in this work. We find that utilizing frequency and recencyof interactions with job postings for combining item embeddingsresults in a robust model with respect to accuracy and diversity, butalso provides the best adapted novelty results
Reiter-Haas Markus, Slawicek Valentin, Lacic Emanuel
2017
Studo Jobs: Enriching Data With Predicted Job Labels
ACM Graz