Calibrating the Predictions for Top-N Recommendations
<|reference_start|>Calibrating the Predictions for Top-N Recommendations: Well-calibrated predictions of user preferences are essential for many applications. Since recommender systems typically select the top-N items for users, calibration for those top-N items, rather than for all items, is important. We show that previous calibration methods result in miscalibrated predictions for the top-N items, despite their excellent calibration performance when evaluated on all items. In this work, we address the miscalibration in the top-N recommended items. We first define evaluation metrics for this objective and then propose a generic method to optimize calibration models focusing on the top-N items. It groups the top-N items by their ranks and optimizes distinct calibration models for each group with rank-dependent training weights. We verify the effectiveness of the proposed method for both explicit and implicit feedback datasets, using diverse classes of recommender models.<|reference_end|>
@article{sato2024calibrating,
    title={Calibrating the Predictions for Top-N Recommendations},
    author={Masahiro Sato},
    journal={arXiv preprint arXiv:2408.11596},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.11596},
    primaryClass={cs.IR cs.LG}
}