Estimating Error and Bias in Offline Evaluation Results

@article{Tian2020EstimatingEA,
  title={Estimating Error and Bias in Offline Evaluation Results},
  author={Mucun Tian and Michael D. Ekstrand},
  journal={Proceedings of the 2020 Conference on Human Information Interaction and Retrieval},
  year={2020}
}
Offline evaluations of recommender systems attempt to estimate users' satisfaction with recommendations using static data from prior user interactions. These evaluations provide researchers and developers with first approximations of the likely performance of a new system and help weed out bad ideas before presenting them to users. However, offline evaluation cannot accurately assess novel, relevant recommendations, because the most novel items were previously unknown to the user, so they are… Expand
LensKit for Python: Next-Generation Software for Recommender Systems Experiments

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The MovieLens Datasets: History and Context
Latent Dirichlet Allocation