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Recommender Systems: A Primer

arXiv 2302.02579 ↗First posted 6 Feb 2023cs.IR cs.AIcited by 0

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Abstract (arXiv)

Personalized recommendations have become a common feature of modern online services, including most major e-commerce sites, media platforms and social networks. Today, due to their high practical relevance, research in the area of recommender systems is flourishing more than ever. However, with the new application scenarios of recommender systems that we observe today, constantly new challenges arise as well, both in terms of algorithmic requirements and with respect to the evaluation of such systems. In this paper, we first provide an overview of the traditional formulation of the recommendation problem. We then review the classical algorithmic paradigms for item retrieval and ranking and elaborate how such systems can be evaluated. Afterwards, we discuss a number of recent developments in recommender systems research, including research on session-based recommendation, biases in recommender systems, and questions regarding the impact and value of recommender systems in practice.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 44 of its 180 references that are in the map. First posted on arXiv 6 Feb 2023.

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44 of 180 references.

References · 44

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32 more
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