Towards Responsible Recommendations: A Daily Updated Ranking Model for Content Issue Detection

Most existing content issue detection models are built upon Multimodal Large Language Models (MLLMs). Although such approaches achieve high accuracy, they often suffer from poor timeliness, limited feature richness, and high serving costs: MLLM-based models are typically updated only monthly or quarterly and rely mainly on intrinsic video features (e.g., sampled frames and captions), underutilizing post-hoc user feedback and aggregated video and author-side signals. To address these challenges, we propose a daily updated ranking model for Content Issue Detection based on a Cross-Order Representation Fusion (CORF) architecture. The model supports day-level updates and integrates user behavior features, video- and author-side information, and MLLM-generated scores as inputs. Compared with MLLM-based approaches, our model is significantly smaller and can efficiently score all videos on a daily basis. Experiments on the Adult-Content category show higher recall at the same level of precision, and online A/B tests further demonstrate reduced user exposure to such content issues, contributing to a safer and more positive viewing experience.