FDDP-RN: Frequency-Domain Denoising and Popularity Bias Correction Recommendation Network

News recommendation is a critical technology that helps users efficiently find content of interest from large candidate pools. Its core objective is to accurately model user reading interests. However, current news recommendation systems typically suffer from two key limitations: (i) they fail to suppress noise from a frequency-domain perspective, and (ii) they lack effective calibration for popularity bias within the embedding space. In this work, we propose a novel frequency-domain denoising and popularity-bias correction recommendation network (FDDP-RN) to address both challenges simultaneously. Our approach introduces spectral analysis into the news encoder. Specifically, we design a filtering mechanism that combines truncation and scaling to enhance high-frequency semantic components, improve text feature representation accuracy, and suppress redundant low-frequency components. In addition, we introduce a norm-scaling factor that dynamically calibrates the embedding distribution of cold-start news items, placing them on an equal footing with popular news items. This effectively improves the exposure of long-tail content without requiring extra user interactions. We conduct extensive experiments on three public datasets, namely, MIND-small, MIND-large, and Adressa. The quantitative results demonstrate that FDDP-RN achieves state-of-the-art performance. Notably, on the Adressa dataset, our model achieves an AUC of 75.36% and an nDCG@10 of 50.11%, outperforming the strongest baseline. Furthermore, cold-start fairness diagnostics on the MIND-small dataset reveal that our mechanism increases the top-10 long-tail exposure rate from 15.3% to 18.1% and reduces the exposure Gini coefficient from 0.991 to 0.987, indicating a better balance among recommendation accuracy, diversity, and fairness.