Collaborative Filtering (CF) recommender models highly depend on user-item interactions to learn CF representations, thus falling short of recommending cold-start items. To address this issue, prior studies mainly introduce item features (e.g., thumbnails) for cold-start item recommendation. They learn a feature extractor on warm-start items to align feature representations with interactions, and then leverage the feature extractor to extract the feature representations of cold-start items for interaction prediction. Unfortunately, the features of cold-start items, especially the popular ones, tend to diverge from those of warm-start ones due to temporal feature shifts, preventing the feature extractor from accurately learning feature representations of cold-start items. To alleviate the impact of temporal feature shifts, we consider using Distributionally Robust Optimization (DRO) to enhance the generation ability of the feature extractor. Nonetheless, existing DRO methods face an inconsistency issue: the worse-case warm-start items emphasized during DRO training might not align well with the cold-start item distribution. To capture the temporal feature shifts and combat this inconsistency issue, we propose a novel temporal DRO with new optimization objectives, namely, 1) to integrate a worst-case factor to improve the worst-case performance, and 2) to devise a shifting factor to capture the shifting trend of item features and enhance the optimization of the potentially popular groups in cold-start items. Substantial experiments on three real-world datasets validate the superiority of our temporal DRO in enhancing the generalization ability of cold-start recommender models. The code is available at https://github.com/Linxyhaha/TDRO/.

翻译：协同过滤推荐模型高度依赖用户-物品交互来学习协同表示，因此在推荐冷启动物品时表现不足。为解决此问题，先前研究主要引入物品特征（如缩略图）进行冷启动推荐。这类方法在热启动物品上训练特征提取器，使其对齐特征表示与交互信息，随后利用该提取器获取冷启动物品的特征表示以进行交互预测。然而，由于时间特征偏移，冷启动物品（尤其是流行物品）的特征往往与热启动物品的特征存在差异，导致特征提取器难以准确学习冷启动物品的特征表示。为缓解时间特征偏移的影响，我们考虑采用分布鲁棒优化来增强特征提取器的泛化能力。然而，现有分布鲁棒优化方法存在不一致性问题：训练中强调的最差情况热启动物品分布可能与冷启动物品分布不匹配。为捕捉时间特征偏移并解决不一致性问题，我们提出包含新优化目标的时间分布鲁棒优化方法：1) 引入最差情况因子以提升最差情况性能；2) 设计偏移因子以捕捉物品特征的偏移趋势并增强冷启动物品中潜在流行群体的优化。在三个真实数据集上的大量实验验证了所提时间分布鲁棒优化方法在增强冷启动推荐模型泛化能力方面的优越性。代码已开源：https://github.com/Linxyhaha/TDRO/。