Beyond Accuracy Optimization: On the Value of Item Embeddings for Student Job Recommendations
This work addresses job recommendations for university students, but it is incremental as it builds on existing content-based methods with minor enhancements.
The paper tackled the problem of recommending jobs to university students by integrating frequency and recency of interactions with job postings to combine neural item embeddings, resulting in a robust model that improved accuracy, diversity, and novelty metrics on a dataset from the Austrian student job portal Studo.
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.