On Sampling Collaborative Filtering Datasets. (arXiv:2201.04768v1 [cs.LG])

We study the practical consequences of dataset sampling strategies on the
ranking performance of recommendation algorithms. Recommender systems are
generally trained and evaluated on samples of larger datasets. Samples are
often taken in a naive or ad-hoc fashion: e.g. by sampling a dataset randomly
or by selecting users or items with many interactions. As we demonstrate,
commonly-used data sampling schemes can have significant consequences on
algorithm performance. Following this observation, this paper makes three main
contributions: (1) characterizing the effect …

arxiv collaborative collaborative filtering datasets