Offline evaluation options for recommender systems

@article{Caamares2020OfflineEO,
  title={Offline evaluation options for recommender systems},
  author={Roc{\'i}o Ca{\~n}amares and Pablo Castells and Alistair Moffat},
  journal={Information Retrieval Journal},
  year={2020},
  volume={23},
  pages={387-410}
}
We undertake a detailed examination of the steps that make up offline experiments for recommender system evaluation, including the manner in which the available ratings are filtered and split into training and test; the selection of a subset of the available users for the evaluation; the choice of strategy to handle the background effects that arise when the system is unable to provide scores for some items or users; the use of either full or condensed output lists for the purposes of scoring… 

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