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过滤表示，因此在推荐冷启动物品时表现不佳。为解决这一问题，先前的研究主要引入物品特征（如缩略图）进行冷启动物品推荐。它们通过暖启动物品学习特征提取器，将特征表示与交互对齐，进而利用该特征提取器提取冷启动物品的特征表示以预测交互。然而，冷启动物品（尤其是流行物品）的特征因时序特征偏移而容易偏离暖启动物品的特征，导致特征提取器无法准确学习冷启动物品的特征表示。为缓解时序特征偏移的影响，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面临不一致性问题：训练过程中分布鲁棒优化所强调的最坏情况暖启动物品可能与冷启动物品分布不匹配。为捕捉时序特征偏移并解决这一不一致性问题，我们提出了一种新颖的时序分布鲁棒优化方法，包含新的优化目标：1）引入最坏情况因子以提升最坏情况性能；2）设计偏移因子以捕捉物品特征的偏移趋势，并增强对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出的时序分布鲁棒优化在提升冷启动推荐模型泛化能力方面的优越性。代码已开源在 https://github.com/Linxyhaha/TDRO/。