  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.
