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In this work, we address the problem of recommending jobs to universitystudents. For this, we explore the utilization of neural item embeddings forthe task of content-based recommendation, and we propose to integrate thefactors of frequency and recency of interactions with job postings to combinethese item embeddings. We evaluate our job recommendation system on a datasetof the Austrian student job portal Studo using prediction accuracy, diversityand an adapted novelty metric. This paper demonstrates that utilizing frequencyand recency of interactions with job postings for combining item embeddingsresults in a robust model with respect to accuracy and diversity, which alsoprovides the best adapted novelty results.

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