  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/.
