Calibrating the Predictions for Top-N Recommendations
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910571932155904 |
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| author | Sato, Masahiro |
| author_facet | Sato, Masahiro |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_11596 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Calibrating the Predictions for Top-N Recommendations Sato, Masahiro Information Retrieval Machine Learning 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. |
| title | Calibrating the Predictions for Top-N Recommendations |
| topic | Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2408.11596 |