Calibrating the Predictions for Top-N Recommendations

TL;DR

This paper addresses the issue of miscalibration in top-N recommendations by proposing a new method that optimizes calibration specifically for top-N items, improving the accuracy of user preference predictions.

Contribution

It introduces a generic approach to calibrate top-N recommendations by rank-dependent optimization, filling a gap left by previous methods that focus on all items.

Findings

Improved calibration for top-N recommendations across datasets

Effective for both explicit and implicit feedback models

Enhances user preference prediction accuracy

Abstract

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.