April 16, 2024, 4:42 a.m. | Claire Schultzberg, Brammert Ottens

cs.LG updates on arXiv.org arxiv.org

arXiv:2404.08671v1 Announce Type: cross
Abstract: Over the last decades has emerged a rich literature on the evaluation of recommendation systems. However, less is written about how to efficiently combine different evaluation methods from this rich field into a single efficient evaluation funnel. In this paper we aim to build intuition for how to choose evaluation methods, by presenting a novel framework that simplifies the reasoning around the evaluation funnel for a recommendation system. Our contribution is twofold. First we present …

abstract aim arxiv build cs.ir cs.lg evaluation however iteration literature paper recommendation recommendation systems recommender systems speed systems type

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