CaliTune is a fine-tuning method for collaborative-filtering recommender systems that improves popularity calibration without relying on computationally expensive post-processing at inference time. Experiments across two backbone models and datasets from the movie and music domains show competitive accuracy–calibration trade-offs, especially when the base model is strongly miscalibrated and recommendation accuracy remains important.