Beyond Accuracy Optimization: On the Value of Item Embeddings for Student Job Recommendations

Emanuel Lacic, Dominik Kowald, Markus Reiter-Haas, Valentin Slawicek, Elisabeth Lex

Publikation: Beitrag in einer FachzeitschriftArtikelForschungBegutachtung

Abstract

In this work, we address the problem of recommending jobs to university students. For this, we explore the utilization of neural item embeddings for the task of content-based recommendation, and we propose to integrate the factors of frequency and recency of interactions with job postings to combine these item embeddings. We evaluate our job recommendation system on a dataset of the Austrian student job portal Studo using prediction accuracy, diversity and an adapted novelty metric. This paper demonstrates that utilizing frequency and recency of interactions with job postings for combining item embeddings results in a robust model with respect to accuracy and diversity, which also provides the best adapted novelty results.
Originalspracheenglisch
FachzeitschriftarXiv.org e-Print archive
PublikationsstatusVeröffentlicht - 21 Nov 2017

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    Beyond Accuracy Optimization : On the Value of Item Embeddings for Student Job Recommendations. / Lacic, Emanuel; Kowald, Dominik; Reiter-Haas, Markus; Slawicek, Valentin; Lex, Elisabeth.

    in: arXiv.org e-Print archive, 21.11.2017.

    Publikation: Beitrag in einer FachzeitschriftArtikelForschungBegutachtung

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