Fine-tuning for Inference-efficient Calibrated Recommendations
Oleg Lesota, Adrian Bajko, Max Walder, Matthias Wenzel, Antonela Tommasel, Markus Schedl · 2025
Calibration is the degree to which a recommender system is able to match the distribution of a certain item attribute among the items consumed by a user with their respective recommendations.Recent work suggests that many recommenders tend to provide miscalibrated recommendations.Furthermore, most approaches aimed at improving calibration adopt the post-processing paradigm, making them computationally costly at the inference time.This work proposes CaliTune, a fine-tuning approach applied to collaborative filtering based recommenders to allow them generate better calibrated recommendations without relying on costly post-processing.We compare CaliTune to an established post-processing approach on two backbone models and datasets from movie and music domains, focusing on popularity calibration.Our results suggest that CaliTune can offer a competitive accuracy-calibration trade-off in several settings, particularly when the backbone model exhibits high miscalibration and accuracy remains important, making it a promising inference-efficient alternative in such cases.